Methods for treating, preventing and predicting risk of developing breast cancer

ABSTRACT

Methods for treating, preventing and predicting a subject&#39;s risk of developing breast cancer are provided. In one aspect, a method of predicting a subject&#39;s risk of developing breast cancer is provided, wherein the method includes: (a) determining the frequency in a breast tissue sample of CD44+, CD24− breast epithelial cells, and (b) predicting that the subject has a relatively elevated risk of developing breast cancer if the frequency of CD44+, CD24− breast epithelial cells is decreased compared to a first control frequency of CD44+, CD24− breast epithelial cells; or (c) predicting that the subject has a relatively reduced risk of developing breast cancer if the frequency of CD44+ breast epithelial cells is increased compared to a second control frequency of CD44+, CD24− breast epithelial cells.

RELATED APPLICATIONS

The present application claims the benefit of U.S. Provisional Patent Application Ser. No. 61/672,973, filed Jul. 18, 2012, which is herein incorporated by reference in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

The research described in this application was supported in part by grants from the National Institutes of Health (Nos. T32 CA009382-26, P01 CA117969, P50 CA89383, P01 CA080111, CA116235-0451, and CA087969), and from a grant from the U.S. Army Congressionally Directed Research (No. W81XWH-07-1-0294). Thus, the U.S. government has certain rights in the invention.

TECHNICAL FIELD

Methods for treating, preventing and predicting a subject's risk of developing breast cancer are provided.

BACKGROUND

Breast cancer is the most common type of cancer among women in the United States, accounting for more than a quarter of all cancers in women. Approximately 2.5 million women in this country are breast cancer survivors, and an estimated 192,370 new cases of breast cancer were diagnosed in women in 2009. Further, estrogen receptor positive (ER+) postmenopausal breast cancer is the most common form of the disease. While advances in treatment have enabled more women to live longer overall and to live longer without disease progression, what is needed in the art are methods for identifying subjects at risk of developing breast cancer before they develop it, and for preventing the development of the disease altogether. Presently, however, very few reliable predictive markers for identifying subjects at high risk for developing breast cancer, such as ER+ or ER− breast cancer, are known.

BRCA1 and BRCA2 mutations are examples of predictive markers that have been correlated with an increased risk of developing breast cancer; however, only 5-10% of breast cancers are thought to be caused by inherited abnormalities in BRCA1 and BRCA2 (i.e. hereditary breast cancer). The remaining approximately 90-95% of all breast cancers are sporadic. Thus, what is needed in the art are novel markers that are useful for identifying subjects having an elevated risk of developing breast cancer, as well as novel targets of breast cancer therapies.

SUMMARY OF THE INVENTION

As follows from the Background section above, there remains a need in the art for methods for predicting a subject's risk of developing breast cancer. Such methods, as well as other, related benefits, are presently provided, as discussed in detail below.

In one aspect, a method of predicting a subject's risk of developing breast cancer is provided, wherein the method includes: (a) determining the frequency in a breast tissue sample of CD44+, CD24− breast epithelial cells, and (b) predicting that the subject has a relatively elevated risk of developing breast cancer if the frequency of CD44+, CD24− breast epithelial cells is decreased compared to a first control frequency of CD44+, CD24− breast epithelial cells; or (c) predicting that the subject has a relatively reduced risk of developing breast cancer if the frequency of CD44+ breast epithelial cells is increased compared to a second control frequency of CD44+, CD24− breast epithelial cells.

In another aspect, the method further includes determining the frequency of CD24+ breast epithelial cells. In one aspect, step (b) includes predicting that the subject has a relatively elevated risk of developing breast cancer if: (i) the frequency of CD44+, CD24− breast epithelial cells is decreased compared to a first control frequency of CD44+, CD24− breast epithelial cells, and (ii) the frequency of CD24+ breast epithelial cells is increased compared to a first control frequency of CD24+ breast epithelial cells; and step (c) includes predicting that the subject has a relatively reduced risk of developing breast cancer if: (i) the frequency of CD44+ breast epithelial cells is increased compared to a second control frequency of CD44+, CD24− breast epithelial cells, and (ii) the frequency of CD24+ breast epithelial cells is decreased compared to a second control frequency of CD24+ breast epithelial cells. In another aspect, step (b) includes: predicting that the subject has a relatively elevated risk of developing breast cancer if the frequency of CD24+ breast epithelial cells is greater than the frequency of CD44+, CD24-breast epithelial cells in the sample; and step (c) includes predicting that the subject has a relatively reduced risk of developing breast cancer if the frequency of CD24+ breast epithelial cells is equal to or less than the frequency of CD44+, CD24− breast epithelial cells in the sample. In still another aspect, the subject is in need of such predicting.

In another aspect, a method of predicting a subject's risk of developing breast cancer is provided. The method includes: (a) determining the frequency in a breast tissue sample of cells of one or more types selected from the group consisting of p27+ breast epithelial cells, Sox17+ breast epithelial cells, Cox2+ breast epithelial cells, Ki67+ breast epithelial cells, ER+, p27+ breast epithelial cells, ER+, Sox17+ breast epithelial cells, ER+, Cox2+ breast epithelial cells, ER+, Ki67+ breast epithelial cells; androgen-receptor-positive (AR+), p27+ breast epithelial cells, AR+, Sox17+ breast epithelial cells, AR+, Cox2+ breast epithelial cells, and AR+, Ki67+ breast epithelial cells; and (b) predicting that the subject has a relatively elevated risk of developing breast cancer if the frequency of the cells of the type is increased compared to a first control frequency of cells of the type; or (c) predicting that the subject has a relatively reduced risk of developing breast cancer if the frequency of the cells of the type is decreased compared to a second control frequency of the cells of the type.

In certain aspects, step (b) includes predicting that the subject has a relatively elevated risk of developing breast cancer if the frequency of p27+ breast epithelial cells is 15 percent (%) or greater of the breast epithelial cells in the sample; and step (c) includes predicting that the subject has a relatively reduced risk of developing breast cancer if the frequency of p27+ breast epithelial cells is less than 15% of the breast epithelial cells in the sample. In other aspects, step (b) includes predicting that the subject has a relatively elevated risk of developing breast cancer if the frequency of p27+ breast epithelial cells is 20 percent (%) or greater of the breast epithelial cells in the sample; and step (c) includes predicting that the subject has a relatively reduced risk of developing breast cancer if the frequency of p27+ breast epithelial cells is less than 20% of the breast epithelial cells in the sample. In still another aspect, step (b) includes predicting that the subject has a relatively elevated risk of developing breast cancer if the frequency of p27+ breast epithelial cells is 25 percent (%) or greater of the breast epithelial cells in the sample; and step (c) includes predicting that the subject has a relatively reduced risk of developing breast cancer if the frequency of p27+ breast epithelial cells is less than 25% of the breast epithelial cells in the sample. In certain aspects, step (b) includes predicting that the subject has a relatively elevated risk of developing breast cancer if the frequency of Ki67+ breast epithelial cells is 2 percent (%) or greater of the breast epithelial cells in the sample; and step (c) includes predicting that the subject has a relatively reduced risk of developing breast cancer if the frequency of Ki67+ breast epithelial cells is less than 2% of the breast epithelial cells in the sample. In yet other aspects, step (b) includes predicting that the subject has a relatively elevated risk of developing breast cancer if: (i) the frequency of p27+ breast epithelial cells is increased compared to a first control frequency of p27+ breast epithelial cells, and (ii) the frequency of Ki67+ breast epithelial cells is increased compared to a first control frequency of Ki67+ breast epithelial cells; and step (c) includes predicting that the subject has a relatively reduced risk of developing breast cancer if: (i) the frequency of p27+ breast epithelial cells is decreased compared to a second control frequency of p27+ breast epithelial cells, and (ii) the frequency of Ki67+ breast epithelial cells is decreased compared to a second control frequency of Ki67+ breast epithelial cells.

In another aspect, a method of predicting a subject's risk of developing breast cancer is provided. The method includes: (a) determining the expression level in a breast tissue sample from a subject of at least one marker selected from the group consisting of p27, Sox17 and Cox2; and (b) predicting that the subject has a relatively elevated risk of developing breast cancer if the expression level of the at least one marker is increased compared to a first control level of the at least one marker; or (c) predicting that the subject has a relatively reduced risk of developing breast cancer if the expression level of the at least one marker is decreased compared to a second control level of the at least one marker. In certain aspects, the expression level determined is the mRNA expression level of the at least one marker. In other aspects, the expression level determined is the protein expression level of the at least one marker. In certain aspects, step (a) includes determining the expression level of at least two (2) markers or all 3 markers selected from the group consisting of p27, Sox17 and Cox2.

In some aspects, step (a) further includes determining the expression level of one or more additional markers having an expression level that is modulated in breast epithelial cells of parous women compared to the levels in breast epithelial cells of nulliparous women. In certain aspects, the sample is enriched for CD44+, CD24− breast epithelial cells or for CD24+ breast epithelial cells prior to the determining. In still other aspects, the sample is enriched for Ki67+ breast epithelial cells or CD44+Ki67+ breast epithelial cells prior to the determining.

In certain aspects, the subject for whom the risk of developing an estrogen-receptor-positive (ER+) breast cancer is being predicted has a BRCA1 and/or a BRCA2 mutation.

In other aspects, a method of predicting a subject's risk of developing breast cancer is provided, which includes determining a parity/nulliparity-associated gene expression signature in a sample containing breast epithelial cells. In certain aspects, the sample is enriched for CD44+ cells, CD24+ cells, or CD10+ cells.

In one aspect, a method of predicting breast cancer disease outcome is provided, including testing for a parity/nulliparity-associated gene expression signature in breast cancer cells.

In another aspect, a method of treating estrogen-receptor-positive (ER+) breast cancer in a subject is provided. The method includes administering to the subject a composition that includes an inhibitor of a pathway that has increased activity in CD44+, CD24− breast epithelial cells of nulliparous women compared to the activity in CD44+, CD24− breast epithelial cells of parous women. In certain aspects, the pathway can be cytoskeleton remodeling, chemokines, androgen signaling, cell adhesion, or Wnt signaling.

In yet another aspect, a method of preventing breast cancer in a subject is provided. The method includes administering to a subject at risk of developing breast cancer an inhibitor of a pathway that has increased activity in breast epithelial cells of nulliparous women compared to breast epithelial cells of parous women. In some aspects, the pathway can be cytoskeleton remodeling, chemokines, androgen signaling, cell adhesion, or Wnt signaling. In certain aspects, the pathway includes a mediator molecule that can be cAMP, EGFR, Cox2, hedgehog (Hh), TGFβ receptor (TGFBR) or IGF receptor (IGFR). In still other aspects, the inhibitor selectively targets CD44+, CD24− breast epithelial cells, CD24+ breast epithelial cells, p27+ breast epithelial cells, or Ki67+ breast epithelial cells. In certain aspects, the cells selectively targeted by the inhibitor are also ER+. In certain aspects, the subject has a BRCA1 or BRCA2 mutation.

In certain aspects, methods of treating or preventing breast cancer in a subject are provided. The methods include administering to a subject an agonist of a pathway that has decreased activity in CD44+, CD24− breast epithelial cells of nulliparous women compared to CD44+, CD24− breast epithelial cells of parous women. In certain aspects, the pathway can be tumor suppression (Hakai/CBLL1, CASP8, SCRIB, LLGL2), DNA repair, PI3K/AKT signaling, or apoptosis. In certain aspects, the agonist selectively targets CD44+, CD24− breast epithelial cells, CD24+ breast epithelial cells, p27+ breast epithelial cells, or Ki67+ breast epithelial cells. In another aspect, the cells selectively targeted by the agonist are also ER+. In certain aspects, the subject has a BRCA1 or BRCA2 mutation.

In any of the above aspects, the breast cancer can be an ER+ or an ER− breast cancer.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. In case of conflict, the present document, including definitions, will control.

All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. The materials, methods, and examples disclosed herein are illustrative only and not intended to be limiting.

The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Preferred methods and materials are described below, although methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present invention. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 contains representative FACS plots for cells stained with fluorescent antibodies specific for CD24 and CD44 from normal breast tissue of nulliparous (upper plot) and parous (lower plot) women.

FIG. 2 contains graphs plotting the frequency (%) of CD44+, CD24+, and CD10+ human breast epithelial cells relative to total human breast epithelial cells from nulliparous and parous women. 10 samples each from nulliparous and parous groups were analyzed, and each dot represents an individual sample. Error bars represent mean±SEM.

FIG. 3 contains dot plots showing a genome-wide view of genes differentially expressed between nulliparous (N) and parous (P) samples in CD44+, CD24− breast epithelial cells (upper left quadrant), CD10+ breast epithelial cells (upper right quadrant), CD24+ breast epithelial cells (lower left quadrant), and stromal fibroblasts (lower right quadrant). Each dot represents a gene. Fold differences between averaged N and P samples and their corresponding p-values are plotted on the y and x-axis, respectively. Vertical lines indicate p=0.05, numbers indicate the number of genes differentially expressed at p<0.05.

FIG. 4A is a three-dimensional projection of the gene expression data onto the first three principal components. Each ball is a different sample; cell type and parity are indicated.

FIG. 4B is a box-and-whisker diagram of the paired Euclidean distance for each of the indicated cell types: CD44+, CD24+, CD10+, and stromal fibroblasts (“stroma”). The middle line within a box represents the median value. The Box is the IQR (interquartile range, 25th and 75th percentile). The top and bottom line of each box plot is the data range: the lowest data still within 1.5 IQR of the lower quantile and the highest data still within 1.5 IQR of the upper quantile. Data shown outside the range are plotted as circles. The Kolmogorov-Smirnov (KS) test was used to determine the significance of difference between CD44+ and other cell types. Statistical significance (p) is indicated.

FIG. 5 is a box-and-whisker diagram of the paired Euclidean distance for the following pair-wise comparisons (from left to right on the x-axis): CD44+, CD24− nulliparous vs. CD10+ nulliparous; CD44+, CD24− nulliparous vs. CD24+ nulliparous; CD44+, CD24-parous vs. CD10+ nulliparous, CD44+, CD24− parous vs. CD24+ nulliparous; (N: nulliparous. P: parous). The middle line within a box represents the median value. The Box is the IQR (interquartile range, 25th and 75th percentile). The top and bottom line of each box plot is the data range: the lowest data still within 1.5 IQR of the lower quantile and the highest data still within 1.5 IQR of the upper quantile. Data shown outside the range are plotted as circles. The Kolmogorov-Smirnov (KS) test was used to determine significance of differences, indicated on the plot (p).

FIG. 6 contains dot plots showing the relative DNA methylation, as determined by qMSP analysis (left panel), and the expression, as determined by qRT-PCR (right panel), of the indicated genes (left panel: TTC9B, RRP15, and AOPKO5; right panel: CDKN1B, PTGS2, COL1A1 and COL3A1) in CD44+, CD24− breast epithelial cells and CD24− breast epithelial cells isolated from multiple nulliparous and parous women, respectively. Relative methylation and expression levels normalized to ACTB and RPL19, respectively, are indicated on the y-axis. The bars mark the median and p-values indicate the statistical significance of the observed differences.

FIG. 7 is a dendrogram showing the hierarchical clustering of Norwegian cohort (GSE18672) based on Pearson correlation using genes differentially expressed in CD44+ cells. Individual patient samples from the cohort are shown (MDG-110, MDG124, etc.); “N-pre” means premenopausal. Clustering analysis using the differentially expressed gene sets divided these samples into two groups, a mixed parous/nulliparous (Nulliparous A) group, and a distinct, nulliparous (Nulliparous B) group.

FIG. 8 is a bar plot of the serum estradiol levels in picograms per milliliter for the samples corresponding to FIG. 7 (Nulliparous A, Nulliparous B and Parous groups).

FIG. 9A is a dendrogram showing the hierarchical clustering of CD44+ cells from parous and nulliparous control women and parous BRCA1 mutation carriers.

FIG. 9B is a dot plot showing the relative frequency of CD44⁺, CD24⁺, and CD10⁺ cells among all breast epithelial cells in samples from nulliparous and parous groups from control and BRCA1/2 mutation carriers. The error bars mark the mean±standard error of the mean (SEM).

FIG. 10 is a dendrogram depicting hierarchical clustering of signaling pathways significantly high in parous or nulliparous samples in any of the four cell types (stromal fibroblasts (“stroma”), CD10+, CD44+ and CD24+ breast epithelial cells) analyzed.

FIG. 11 is a heat map depicting unsupervised clustering of signaling pathways significantly down- or upregulated in parous compared to nulliparous samples in any of the four cell types types (stromal fibroblasts (“stroma”), CD10+, CD44+ and CD24+ breast epithelial cells) analyzed. Gray scale indicates −log p value of enrichment. Rectangles highlight cell type-specific or common altered pathways.

FIG. 12 contains graphs showing the relative enrichment (left panel) and relative connectivity (right panel) of the indicated protein classes in nulliparous and parous samples in each of the four cells types (stromal fibroblasts (“stroma”), CD10+, CD44+ and CD24+ breast epithelial cells) analyzed. X-axes indicate −log 10 p-values for enrichment (left panel) with the listed protein classes and the number of overconnected objects, defined as proteins with higher than expected number of interactions, in each functional category within each group (right panel), respectively.

FIG. 13 is an integrated map of statistically significant (P-val<0.05) pathways enriched in genes highly expressed in CD44+ nulliparous cells along with DNA methylation patterns. Important pathways highly active in CD44+ nulliparous cells potentially regulated by DNA methylation include PI3K signaling and TCF/Lef signaling. Highly expressed genes, and promoter and gene body hypo and hyper-methylation are indicated.

FIG. 14 is an integrated map of statistically significant (P-val<0.05) pathways enriched in genes highly expressed in CD44+ parous cells along with DNA methylation patterns. Active pathways potentially regulated by DNA methylation in CD44+ parous cells include TGFB2 signaling. Highly expressed genes, and promoter and gene body hypo and hyper-methylation are indicated.

FIG. 15A is a Venn diagram depicting the number of unique and common pathways high in CD44+ nulliparous cells and in mammary glands of virgin rats, respectively.

FIG. 15B is a list of top common pathways downregulated in CD44+ cells and mammary glands from parous women and rats, respectively. Names of the pathways and p-values of enrichment are indicated.

FIG. 16 contains dot plots showing a genome-wide view of differentially methylated genes in CD44+ (upper panel) and CD24+ (lower panel) cells between nulliparous and parous samples. All MSDK sites are plotted on the x-axis in the order of p-values of the difference between nulliparous and parous samples in CD44+ or CD24+ cells. Log ratios of averaged MSDK counts in three N and three P samples are plotted on the y-axis. Vertical lines indicate p=0.01 and the numbers of significant DMRs (p<0.01) are shown in the upper and lower right corners of the plots.

FIG. 17 is a heat map showing the pathways enriched by genes associated with gene body or promoter DMRs in CD44+ cells from nulliparous and parous samples.

FIG. 18 contains graphs quantifying (in arbitrary units) the expression of p27, Sox17 and Cox2 in CD44+ and CD24+ breast epithelial cells in premenopausal nulliparous (NP) and parous (P) women. Horizontal bars indicate the median, vertical bars indicate SEM, and p-values indicate the statistical significance of the observed differences.

FIG. 19 is a graph showing the frequencies (% of total breast epithelial cells) of p27+ and Ki67+ cells in nulliparous (NP) and parous (P) breast tissue samples. Horizontal bars indicate the median, vertical bars indicate SEM, and p-values of differences between nulliparous and parous groups are indicated.

FIG. 20 contains graphs quantifying the expression of p27 (in arbitrary units) and the frequencies (% of total breast epithelial cells) of p27+ and Ki67+ cells in CD44+ and CD24+ breast epithelial cells in postmenopausal nulliparous (NP) and parous (P) women (FIG. 20)

FIG. 21 contains graphs quantifying the expression of p27 (in arbitrary units) and the frequencies (% of total breast epithelial cells) of p27+ and Ki67+ cells in high and low density areas of the same breast from premenopausal parous women.

FIG. 22 contains bar graphs quantifying the frequencies (% of total breast epithelial cells) of p27+ and ER+ cells in each group of samples (nulliparous, parous, women in follicular or luteal phase of menstrual cycle, oocyte donor, early pregnancy, late pregnancy, BRCA1+ mutation carriers and BRCA-2 mutation carriers). Horizontal bars indicate the median, vertical bars mark the SEM, and asterisks indicate significant (p<0.05, t-test or Fisher exact test) differences between groups of 4-8 samples.

FIG. 23A is a bar graph quantifying frequencies (fraction (%) of total breast epithelial cells) of p27+, androgen receptor (AR)+, and p27+AR+ cells in each set of samples (nulliparous, parous, and BRCA1+ mutation carriers).

FIG. 23B contains bar graphs quantifying frequencies (% of total breast epithelial cells) of p27+, Ki67+, and p27+Ki67+ cells in each set of samples (sample collected from women in the follicular or luteal phase of the menstrual cycle, oocyte donor and women in early pregnancy).

FIG. 23C contains bar graphs quantifying the frequency of p27+, Ki67+, and p27+Ki67+ cells in the breast tissue of premenopausal and postmenopausal nulliparous (NP) or parous (P) women in different phases of the menstrual cycle (i.e., follicular (“Foll”) and luteal (“Lut”)) or with breast cancer (BC) or without (cont); asterisks mark p<0.05.

FIG. 24 contains bar graphs quantifying the frequency (% of total breast epithelial cells) of BrdU+, Ki67+, and p27+ cells in each of the indicated conditions (control, inhibition of cAMP, EGFR, Cox2, Hh, TGFβ, Wnt, or IGFR in normal breast tissues incubated in a tissue explant culture model with the relevant inhibitor); * indicates p<0.05 and bars indicate SEM.

FIG. 25 contains bar graphs quantifying the frequency (% of total breast epithelial cells) of pSMAD2+ cells, or the mean fluorescence intensity of pEGFR and Axin 2 in breast epithelial tissue treated with control (C) or inhibitor (I) (inhibitor of TGFb, EGFR or Wnt, from top graph to bottom graph).

FIG. 26A contains line graphs plotting the RGB spectra demonstrating overlap between the expression of p27 and the indicated marker (in the top panels: circles mark the line for pSMAD2, triangles mark the line for p27, and squares mark the line for DAPI; in the middle panels: circles mark the line for pEGFR, triangles mark the line for p27, squares mark the line for DAPI; in the lower panel: circles mark the line for axin2 and squares mark the line for DAPI); left graphs are control groups and right graphs are treated with the indicated inhibitor. In all graphs, intensity is plotted on the y-axis and distance (in pixels) in plotted on the x-axis.

FIG. 26B contains a bar graph quantifying the frequency (%) of p27+ cells in tissue slices from 3-4 independent cases treated with hormones mimicking the indicated physiologic levels (control, follicular phase, luteal phase, and pregnancy) in women. Asterisks indicated significant (p≦0.05) differences.

FIG. 26C contains bar graphs quantifying the frequency (% of all breast epithelial cells) of p27+, Ki67+, and p27+Ki67+ cells in tissue slice cultures treated with Shh or Tamoxifen; asterisks indicate a statistical significance of p≦0.05.

FIG. 27 contains Kaplan-Meier plots depicting the probability of breast cancer-specific survival among women with invasive ER+ (left panel) or ER− (right panel) breast cancer by parity in the Nurses' Health Study (1976-2006). The p-value of the difference between the two survival curves overall was calculated with use of the log-rank test. Beneath each plot the number of parous and nulliparous women alive at each of the time points shown on the x-axes of the plots (beginning at 5 years) is shown.

FIGS. 28 and 29A-C contain heat maps (left panel) and Kaplan-Meier plots with their corresponding log-rank test p-values (right panel) showing a significant association of the presence of a parity/nulliparity-related gene signature with overall survival in the indicated cohorts of breast cancer patients with ER+ tumors. In each figure, the top heat map shows the signature from down regulated genes in parous subjects and the bottom heat map from up group genes. The bars above the heat maps indicate the two distinct patients groups separated by the co-expression of the signature (light gray (left bar on heat map, upper line on Kaplan-Meier plots): better survival group; dark gray (right bar on heat map, lower line on Kaplan-Meier plots): worse survival group). The bar at the right side of heat map, divided into an upper and lower group, indicates effect of parity on genes in breast cancer progression. The upper group indicates parity induces gene expression level change in the same trend as breast cancer progression. The lower group indicates parity induces gene expression level change in the opposite trend as breast cancer progression. Black bars (beneath the heat maps) indicate death. The genes shown in the heat maps (the parity/nulliparity-related gene signature) are shown in Table 18, below, which shows the gene symbol, gene description, gene expression pattern (i.e., high in parous and nulliparous samples), and prognostic values (good or bad prognosis) for each of the genes.

FIG. 30 contains a diagram showing the timeline for simulations in a mathematical model of the dynamics of proliferating mammary epithelial cells that can accumulate the changes leading to cancer initiation, run from the time of menarche at 12.6 years through cancer initiation or death at 80.9 years. The earliest time of pregnancy is at menarche; the latest time is right before menopause at 51.3 years.

FIGS. 31-33 are schematic representations of a mathematical model of the dynamics of proliferating mammary epithelial cells that can accumulate the changes leading to cancer initiation. In FIG. 31, initially, there are N wild-type stem cells (top of schematic), which give rise to a differentiation cascade of 2^(z+1)−1 wild-type luminal progenitor cells (triangular, lower region). Darkening gray gradations refer to successively more differentiated cells and serve to clarify a single time step of the stochastic process. In FIG. 32, “WT” means wild-type (non-mutated) stem cell and “f_(mut)” means mutant progenitor cell. Division during pregnancy is indicated by “z_(preg)”; z is the number of cell divisions; K indicates the number of cell divisions from the first progeny of the stem cell (k=0) to the terminally differentiated cell (darkest gray).

FIG. 34 is a bar graph quantifying the effect the indicated parameters of the mathematical model described in Example 10 (N value, Zpreg, and p) have on the relative probability of cancer initiation (per duct) relative to nulliparous women. The default values were: N=8, p=10⁻², Z_(preg)=2.

FIG. 35 is a line graph plotting the likelihood (relative probability) of cancer initiation relative to nulliparous (y-axis) against time of first pregnancy after menarche (years) on the x-axis for the indicated starting number of stem cells (N=5, N=8, and N=10).

FIG. 36 is a line graph plotting the likelihood (relative probability) of cancer initiation relative to nulliparous (y-axis) against time of first pregnancy after menarche (years) on the x-axis for the indicated probabilities of stem cell differentiation (p=0.1, p=0.01, and p=0.001)

FIG. 37 is a line graph plotting the likelihood (relative probability) of cancer initiation relative to nulliparous (y-axis) against time of first pregnancy after menarche (years) on the x-axis for the indicated number of additional cell divisions during pregnancy (3 and 2).

DETAILED DESCRIPTION

Various aspects of the invention are described below.

I. OVERVIEW

A single full-term pregnancy in early adulthood decreases the risk of estrogen receptor (ER)-positive (+) postmenopausal breast cancer, the most common form of the disease. Age at first pregnancy is critical, as the protective effect decreases after the mid 20s, and women aged >35 years at first birth have increased risk of both ER+ and ER− breast cancer. Parity-associated risk is also influenced by germline variants, as BRCA1 and BRCA2 mutation carriers do not experience the same decrease in risk reduction as does the general population. These human epidemiological data suggest that pregnancy induces long-lasting effects in the normal breast epithelium and that ER+ and ER− tumors might have a different cell of origin. The protective effect of parity is also observed in animal models, where its protective effect can be mimicked by hormonal factors in the absence of gestation.

The cellular and molecular mechanisms that underlie pregnancy and hormone-induced refractoriness to carcinogens are largely undefined. Several hypotheses have been proposed including the induction of differentiation, decreased susceptibility to carcinogens, a decrease in cell proliferation and in the number of mammary epithelial stem cells, an altered systemic environment due to a decrease in circulating growth hormone and other endocrine factors, and permanent molecular changes leading to alterations in cell fate. Almost all studies investigating pregnancy-induced changes and the breast cancer preventative effects of pregnancy have been conducted in rodent models and most of them have focused only on the mammary gland. Global gene expression profiling of mammary glands from virgin and parous rats identified changes in TGFβ and IGF signaling, and in the expression of extracellular matrix proteins.

Related studies conducted in humans also identified consistent differences in gene expression profiles between nulliparous and parous women (see Asztalos et al. (2010) Cancer Prev Res (Phila) 3, 301-311; Belitskaya-Levy et al. (2011) Cancer Prev Res (Phila) 4, 1457-1464; Russo et al. (2008) Cancer Epidemiol Biomarkers Prev 17, 51-66; and Russo et al. (2011) Int J Cancer; October 25; E-pub ahead of print). Because those studies used total mammary gland or mammary organoids, which are composed of multiple cell types the cellular origin of these gene expression differences remains unknown. Emerging data indicate that mammary epithelial progenitor or stem cells are the cell of origin of breast carcinomas. Studies assessing changes in mammary epithelial stem cells following pregnancy, however, have been conducted only in mice and thus far have been inconclusive. Thus, the effect of pregnancy on the number and functional properties of murine mammary epithelial progenitors is still elusive and it has not yet been analyzed in humans.

It is presently discovered that parity has a pronounced effect on CD44+ cells with progenitor features. As demonstrated in the present Examples, most of the differences in CD44+ cells between nulliparous and parous samples related to transcriptional repression and downregulation of genes and pathways important for stem cell function, many of which also play a role in tumorigenesis, including EGF, IGF, Hh, and TGFβ signaling. High circulating IGF-1 levels have been associated with increased risk of ER+ breast cancer, and germline polymorphism in members of the TGFβ signaling pathway have also been described to influence breast cancer susceptibility.

The present Examples also demonstrate that parity not only influences the risk of developing breast cancer, but potentially even the type of tumor and associated clinical outcome in breast cancer patients. Moreover, based on the genomic profiling and functional validation results in tissue explant cultures shown in the present Examples, the pathways that were identified as less active in parous women can be used for risk stratification and for chemoprevention in high-risk women, as their inhibition will mimic the cancer-reducing effects of parity.

The present Examples also demonstrate a significant decrease in the number of p27+ cells in breast tissues of parous women, which seems paradoxical as p27 (also known as CDKN1B/p27(kip1)) is a bona fide tumor suppressor and potent inhibitor of cell cycle progression. p27 has been shown to play an important role in stem cells, best characterized in the hematopoietic system, where loss of p27 increases the number of transit amplifying progenitors but not that of stem cells. In the mouse mammary gland, p27 deficiency leads to hypoplasia and impaired ductal branching and lobulo-alveolar differentiation, a phenotype consistent with a putative role in regulating the number and proliferation of mammary epithelial progenitors, although this has not been investigated.

While not intending to be bound by any one particular theory or mechanism of action, based on the data in the present Examples, it is thought that p27 regulates the proliferation and pool size of hormone-responsive breast epithelial progenitors; thus, the lower number of p27+ cells in parous women reflects a decrease in the number of quiescent progenitors with proliferative potential, which may contribute to their decrease in breast cancer risk. High p27 levels and quiescence are maintained in these cells by TGFβ signaling, as implied by the co-expression of pSmad2 with p27 and the increase in BrdU incorporation with concomitant decrease in p27 (Example 9).

It is also presently discovered that the frequency of p27+ cells was high in control nulliparous women and even higher in BRCA1 and BRCA2 mutation carriers even though these different groups of women are predisposed to different types of breast cancer (Example 2). Nulliparous women have increased risk of postmenopausal ER+ breast cancer, whereas BRCA1 mutation carriers most commonly have ER− basal-like tumors. However, recently published studies analyzing the potential cell-of-origin of BRCA1-associated breast cancer in animal models and in humans have found that even these basal-like tumors may initiate from luminal progenitors. The present Examples demonstrate increased frequency of hormone responsive p27+ cells in all high-risk women, supporting these hypotheses.

Thus, the number of p27+ breast epithelial progenitor (CD44+) cells in the normal breast and the activity of pathways that regulate the number of p27+ cells can be used as markers for predicting the risk of developing breast cancer (e.g., ER+ breast cancer or ER− breast cancer), as novel targets for cancer preventive and treatment strategies (e.g. therapeutic intervention), and for monitoring the efficacy of such preventive and treatment strategies. Furthermore, the pathways identified herein, e.g., a TGFβ pathway, can be exploited for breast cancer prevention, as they can be modulated to deplete p27+ cells with progenitor features and consequently decrease breast cancer risk.

II. DEFINITIONS

As used herein, the term “estrogen-receptor-positive (ER+) breast cancer” means a cancer wherein at least one cancer cell expresses the estrogen receptor. As used herein, the term “estrogen-receptor-negative (ER−) breast cancer” means a cancer wherein the cancer cells do not express the estrogen receptor.

As used herein, a “breast tissue sample” can include, but is not limited to, histological sections of normal breast tissue, e.g., healthy breast tissue, tumors or cancer cell-containing tissue, whole or soluble fractions of tissue or cell (e.g., cancer cell) lysates, cell subfractions (e.g., mitochondrial or nuclear subfractions), whole or soluble fractions of tissue or cell (e.g., cancer cell) subfraction lysates can be analyzed.

As used herein, a cell that is “positive” for a marker, such as, e.g., a CD44+, p27+, CD24+, or CD10+ cell, expresses the marker at the mRNA and/or protein level.

As used herein, breast “stromal cells” are breast cells other than epithelial cells.

As used herein, the term “subject” means any animal, including any vertebrate or mammal, and, in particular, a human, and can also be referred to, e.g., as an individual or patient. Typically, not necessarily, the subject is female. A subject in “need of such predicting” i.e., a subject in need of predicting the subject's risk of developing breast cancer, can be, e.g., a subject with a family history of breast cancer, a subject who has not been tested for and/or has not been diagnosed with breast cancer, a subject who wishes to know their risk of developing breast cancer, e.g., ER+ or ER− breast cancer, and/or a subject undergoing a routine health screen by, e.g., their attending physician, and/or a subject undergoing a therapy (e.g., raloxifen or tamoxifen) for the treatment and/or prevention of cancer (e.g., breast cancer).

As used herein, a subject (e.g., patient) having a characteristic (as described herein) that results in a “relatively elevated risk of developing breast cancer,” (e.g., ER+ or ER− breast cancer) has a greater risk of developing breast cancer than a subject not having that characteristic. Conversely, a subject having a characteristic (as described herein) that results in a “relatively reduced risk of developing breast cancer,” has a lesser risk of developing breast cancer than a subject not having that characteristic.

As used herein, a “parous” subject is a woman who has carried a pregnancy for at least 37 weeks of gestation, one or more times. As used herein, a “nulliparous” subject is a woman who has never carried a pregnancy for at least 37 weeks gestation.

As used herein, a “first control frequency” of a cell type (e.g., CD44+ or CD24+ cells) is the frequency of the cell type in a comparable sample from a patient or the average frequency in comparable samples from a plurality of patients known to be at low risk of developing breast cancer (e.g., parous women not expressing BRCA1 or BRCA2 mutations). “Comparable sample” typically means the same sample type (e.g., tumor biopsy or histological section from the same tissue (e.g. breast tissue). The first control frequency can also be a “predetermined reference frequency” (i.e., standard) to which the frequency of the cell type in a test sample is compared. As used herein, a “second control frequency” of a cell type (e.g., CD44+ or CD24+ cells) is the frequency of the cell type in a comparable sample from a patient or the average frequency in comparable samples from a plurality of patients known to be at high risk of developing breast cancer (e.g., nulliparous women).

As used herein, the “expression level” of a marker, such as, e.g., CD44, CD24, CD10, p27, Ki67, Sox17, Cox2, cAMP, EGFR, TGFBR, Cox2, Hh, and IGFR, etc. means the mRNA and/or protein expression level of the marker, or the measurable level of the marker in a sample (e.g., the level of cAMP can be detected by immunoassay), which can be determined by any suitable method known in the art, such as, but not limited to Northern blot, polymerase chain reaction (PCR), e.g., quantitative real-time, “QPCR”, Western blot, immunoassay (e.g., ELISA), immunohistochemistry, cell immunostaining and fluorescence activated cell sorting (FACS), etc.

As used herein, a “substantially altered” level of expression of a gene in a first cell (or first tissue) compared to a second cell (or second tissue) is an at least 2-fold (e.g., at least: 2-; 3-; 4-; 5-; 6-; 7-; 8-; 9-; 10-; 15-; 20-; 30-; 40-; 50-; 75-; 100-; 200-; 500-; 1,000-; 2000-; 5,000-; or 10,000-fold) altered level of expression of the gene. It is understood that the alteration can be an increase or a decrease.

As used herein, the term “selectively targets”, e.g., in the context of a specific cell type (e.g., CD44+, CD24− breast epithelial cells, p27+ breast epithelial cells, etc.) means the targeting agent (e.g., an inhibitor or agonist) mediates an effect on the specific target cell, but not on other cells. Thus, for example, an inhibitor that selectively targets CD44+ cells will mediate an effect (e.g. inhibition, e.g., of proliferation) on CD44+ cells, but not on CD44− cells. Such selective targeting can be achieved, e.g., by conjugating the inhibitor to an antibody that specifically binds to the target cell (e.g., an anti-CD44 antibody), as well as by other methods known in the art.

As used herein, “treating” or “treatment” of a state, disorder or condition includes: (1) preventing or delaying the appearance of clinical or sub-clinical symptoms of the state, disorder or condition developing in a mammal that may be afflicted with or predisposed to the state, disorder or condition but does not yet experience or display clinical or subclinical symptoms of the state, disorder or condition; and/or (2) inhibiting the state, disorder or condition, i.e., arresting, reducing or delaying the development of the disease or a relapse thereof (in case of maintenance treatment) or at least one clinical or sub-clinical symptom thereof; and/or (3) relieving the disease, i.e., causing regression of the state, disorder or condition or at least one of its clinical or sub-clinical symptoms; and/or (4) causing a decrease in the severity of one or more symptoms of the disease. The benefit to a subject to be treated is either statistically significant or at least perceptible to the patient or to the physician.

As used herein, the term “treating cancer” (e.g., treating an ER+ or ER− breast cancer) means causing a partial or complete decrease in the rate of growth of a tumor, and/or in the size of the tumor and/or in the rate of local or distant tumor metastasis in the presence of an inhibitor of the invention, and/or any decrease in tumor survival.

As used herein, the term “preventing a disease” (e.g., preventing ER+ or ER− breast cancer) in a subject means for example, to stop the development of one or more symptoms of a disease in a subject before they occur or are detectable, e.g., by the patient or the patient's doctor. Preferably, the disease (e.g., cancer) does not develop at all, i.e., no symptoms of the disease are detectable. However, it can also result in delaying or slowing of the development of one or more symptoms of the disease. Alternatively, or in addition, it can result in the decreasing of the severity of one or more subsequently developed symptoms.

As used herein, a “pathway that has decreased activity”, e.g., in breast epithelial cells (e.g., CD44+, CD24− breast epithelial cells)) of parous or nulliparous women means a pathway involving one or more genes or polypeptides mediating a function in the pathway that have reduced level of expression and/or activity. Non-limiting examples of such pathways are exemplified in Tables 10 and 11.

As used herein, the term “parity/nulliparity-related gene signature” means the known expression level of a group of two or more genes in breast epithelial cells of parous and nulliparous women (as disclosed herein). For example, the group of genes that were shown to be upregulated or downregulated in FIG. 28, or a subgroup of the genes, are part of such parity/nulliparity-related gene signature. The genes shown in FIG. 28 are summarized in Table 18. Of course, the skilled artisan will appreciate that a parity/nulliparity-related gene signature can, but does not necessarily, include all of the genes shown in Table 18. Preferably, the signature includes 2 or more, 3 or more, 4 or more, 5 or more, 10 or more, 15 or more, 20 or more, 30 or more, 40 or more, 50 or more, or 100 or more of the genes shown in Table 18.

As used herein “combination therapy” means the treatment of a subject in need of treatment with a certain composition or drug in which the subject is treated or given one or more other compositions or drugs for the disease in conjunction with the first and/or in conjunction with one or more other therapies, such as, e.g., a cancer therapy such as chemotherapy, radiation therapy, and/or surgery. Such combination therapy can be sequential therapy wherein the patient is treated first with one treatment modality (e.g., drug or therapy), and then the other (e.g., drug or therapy), and so on, or all drugs and/or therapies can be administered simultaneously. In either case, these drugs and/or therapies are said to be “coadministered.” It is to be understood that “coadministered” does not necessarily mean that the drugs and/or therapies are administered in a combined form (i.e., they may be administered separately or together to the same or different sites at the same or different times).

The term “pharmaceutically acceptable derivative” as used herein means any pharmaceutically acceptable salt, solvate or prodrug, e.g., ester, of a compound of the invention, which upon administration to the recipient is capable of providing (directly or indirectly) a compound of the invention, or an active metabolite or residue thereof. Such derivatives are recognizable to those skilled in the art, without undue experimentation. Nevertheless, reference is made to the teaching of Burger's Medicinal Chemistry and Drug Discovery, 5th Edition, Vol 1: Principles and Practice, which is incorporated herein by reference to the extent of teaching such derivatives. Pharmaceutically acceptable derivatives include salts, solvates, esters, carbamates, and/or phosphate esters.

As used herein the terms “therapeutically effective” and “effective amount”, used interchangeably, applied to a dose or amount refer to a quantity of a composition, compound or pharmaceutical formulation that is sufficient to result in a desired activity upon administration to an animal in need thereof. Within the context of the present invention, the term “therapeutically effective” refers to that quantity of a composition, compound or pharmaceutical formulation that is sufficient to reduce or eliminate at least one symptom of a disease or condition specified herein, e.g., breast cancer such as ER+ or ER− breast cancer. When a combination of active ingredients is administered, the effective amount of the combination may or may not include amounts of each ingredient that would have been effective if administered individually. The dosage of the therapeutic formulation will vary, depending upon the nature of the disease or condition, the patient's medical history, the frequency of administration, the manner of administration, the clearance of the agent from the host, and the like. The initial dose may be larger, followed by smaller maintenance doses. The dose may be administered, e.g., weekly, biweekly, daily, semi-weekly, etc., to maintain an effective dosage level.

Therapeutically effective dosages can be determined stepwise by combinations of approaches such as (i) characterization of effective doses of the composition or compound in in vitro cell culture assays using tumor cell growth and/or survival as a readout followed by (ii) characterization in animal studies using tumor growth inhibition and/or animal survival as a readout, followed by (iii) characterization in human trials using enhanced tumor growth inhibition and/or enhanced cancer survival rates as a readout.

The term “nucleic acid hybridization” refers to the pairing of complementary strands of nucleic acids. The mechanism of pairing involves hydrogen bonding, which may be Watson-Crick, Hoogsteen or reversed Hoogsteen hydrogen bonding, between complementary nucleoside or nucleotide bases (nucleobases) of the strands of nucleic acids. For example, adenine and thymine are complementary nucleobases that pair through the formation of hydrogen bonds. Hybridization can occur under varying circumstances. Nucleic acid molecules are “hybridizable” to each other when at least one strand of one nucleic acid molecule can form hydrogen bonds with the complementary bases of another nucleic acid molecule under defined stringency conditions. Stringency of hybridization is determined, e.g., by (i) the temperature at which hybridization and/or washing is performed, and (ii) the ionic strength and (iii) concentration of denaturants such as formamide of the hybridization and washing solutions, as well as other parameters. Hybridization requires that the two strands contain substantially complementary sequences. Depending on the stringency of hybridization, however, some degree of mismatches may be tolerated. Under “low stringency” conditions, a greater percentage of mismatches are tolerable (i.e., will not prevent formation of an anti-parallel hybrid). See Molecular Biology of the Cell, Alberts et al., 3rd ed., New York and London: Garland Publ., 1994, Ch. 7.

Typically, hybridization of two strands at high stringency requires that the sequences exhibit a high degree of complementarity over an extended portion of their length. Examples of high stringency conditions include: hybridization to filter-bound DNA in 0.5 M NaHPO4, 7% SDS, 1 mM EDTA at 65° C., followed by washing in 0.1×SSC/0.1% SDS (where 1×SSC is 0.15 M NaCl, 0.15 M Na citrate) at 68° C. or for oligonucleotide (oligo) inhibitors washing in 6×SSC/0.5% sodium pyrophosphate at about 37° C. (for 14 nucleotide-long oligos), at about 48° C. (for about 17 nucleotide-long oligos), at about 55° C. (for 20 nucleotide-long oligos), and at about 60° C. (for 23 nucleotide-long oligos).

Conditions of intermediate or moderate stringency (such as, for example, an aqueous solution of 2×SSC at 65° C.; alternatively, for example, hybridization to filter-bound DNA in 0.5 M NaHPO4, 7% SDS, 1 mM EDTA at 65° C. followed by washing in 0.2×SSC/0.1% SDS at 42° C.) and low stringency (such as, for example, an aqueous solution of 2×SSC at 55° C.), require correspondingly less overall complementarity for hybridization to occur between two sequences. Specific temperature and salt conditions for any given stringency hybridization reaction depend on the concentration of the target DNA or RNA molecule and length and base composition of the probe, and are normally determined empirically in preliminary experiments, which are routine (see Southern, J. Mol. Biol. 1975; 98:503; Sambrook et al., Molecular Cloning: A Laboratory Manual, 2nd ed., vol. 2, ch. 9.50, CSH Laboratory Press, 1989; Ausubel et al. (eds.), 1989, Current Protocols in Molecular Biology, Vol. I, Green Publishing Associates, Inc., and John Wiley & Sons, Inc., New York, at p. 2.10.3). An extensive guide to the hybridization of nucleic acids is found in, e.g., Tijssen (1993) Laboratory Techniques in Biochemistry and Molecular Biology—Hybridization with Nucleic Acid Probes part I, chapt 2, “Overview of principles of hybridization and the strategy of nucleic acid probe assays,” Elsevier, N.Y. (“Tijssen”).

As used herein, the term “standard hybridization conditions” refers to hybridization conditions that allow hybridization of two nucleotide molecules having at least 50% sequence identity. According to a specific embodiment, hybridization conditions of higher stringency may be used to allow hybridization of only sequences having at least 75% sequence identity, at least 80% sequence identity, at least 90% sequence identity, at least 95% sequence identity, or at least 99% sequence identity.

As used herein, the phrase “under hybridization conditions” means under conditions that facilitate specific hybridization of a subset of capture oligonucleotides to complementary sequences present in the cDNA or cRNA. The terms “hybridizing specifically to” and “specific hybridization” and “selectively hybridize to,” as used herein refer to the binding, duplexing, or hybridizing of a nucleic acid molecule preferentially to a particular nucleotide sequence under at least moderately stringent conditions, and preferably, highly stringent conditions, as discussed above.

“Polypeptide” and “protein” are used interchangeably and mean any peptide-linked chain of amino acids, regardless of length or post-translational modification.

As used herein, the term “nucleic acid” or “oligonucleotide” refers to a deoxyribonucleotide or ribonucleotide in either single- or double-stranded form. The term also encompasses nucleic-acid-like structures with synthetic backbones. DNA backbone analogues provided by the invention include phosphodiester, phosphorothioate, phosphorodithioate, methylphosphonate, phosphoramidate, alkyl phosphotriester, sulfamate, 3′-thioacetal, methylene(methylimino), 3′-N-carbamate, morpholino carbamate, and peptide nucleic acids (PNAs); see Oligonucleotides and Analogues, a Practical Approach, edited by F. Eckstein, IRL Press at Oxford University Press (1991); Antisense Strategies, Annals of the New York Academy of Sciences, Volume 600, Eds. Baserga and Denhardt (NYAS 1992); Milligan (1993) J. Med. Chem. 36:1923-1937; Antisense Research and Applications (1993, CRC Press). PNAs contain non-ionic backbones, such as N-(2-aminoethyl) glycine units. Phosphorothioate linkages are described in WO 97/03211; WO 96/39154; Mata (1997) Toxicol. Appl. Pharmacol. 144:189-197. Other synthetic backbones encompassed by the term include methyl-phosphonate linkages or alternating methylphosphonate and phosphodiester linkages (Strauss-Soukup (1997) Biochemistry 36:8692-8698), and benzylphosphonate linkages (Samstag (1996) Antisense Nucleic Acid Drug Dev 6:153-156). The term nucleic acid is used interchangeably with cDNA, cRNA, mRNA, oligonucleotide, probe and amplification product.

III. CELL MARKERS

In certain embodiments, it is desirable to detect the presence and/or expression level of one or more cell markers (e.g., estrogen receptor (ER), p27, CD24, CD44, CD10, Ki67, BRCA1, BRCA2, etc.) associated with breast epithelial cells and/or breast cancer (e.g., ER+ or ER− breast cancer). Moreover, the present document features methods in which the relative numbers of cells expressing one or more of these markers are determined. The nucleic acid and amino acid sequences for such markers are known and have been described, and the GenBank® Accession Nos. of exemplary nucleic acid and amino acid sequences for the human markers are provided in Table 1, below.

TABLE 1 Exemplary GenBank ® Accession Numbers Breast Cancer-Associated Markers Nucleic Acid Amino Acid GenBank ® SEQ Corresponding GenBank ® SEQ Gene Name Accession No. ID NO Polypeptide Name Accession No. ID NO CD24 BG327863 1 Sialoglycoprotein ACI46150.1 2 CD10 NM_007289.2 3 Neprilysin NP_009220.2 4 CD44 BC004372 5 CD44 AAB30429.1 6 P27/CDKN1B BC001971 7 CDKN1B CAG33680.1 8 Ki67 (MKI67) AU152107 9 KI67 antigen CAD99007.1 10 Homo sapiens NM_022454.3 11 transcription factor NP_071899 12 SRY (sex SOX-17 determining region Y)-box 17 (SOX17) Prostaglandin- NM_000963 13 prostaglandin G/H NP_000954 14 endoperoxide synthase 2 synthase 2 precursor (prostaglandin G/H synthase and cyclooxygenase) (PTGS2) Epidermal NM_005228 15 Epidermal growth NP_005219.2 19 Growth Factor NM_201282 16 factor receptor NP_958439.1 20 Receptor P NM_201283.1 17 NP_958440.1 21 (EGFR) NM_201284 18 NP_958441.1 22 sonic hedgehog NM_000193 23 Sonic hedgehog NP_000184 24 protein (SHH) protein insulin-like NM_000875 25 Insulin like NP000866 26 growth factor 1 Growth factor receptor receptor (IGF1R) transforming NM_004612 27 Transforming NP_004603 29 growth factor, NM_001130916 28 Growth factor NP_001124388 30 beta receptor 1 receptor beta (TGFBR1) receptor estrogen NM_000125.3 31 Estrogen Receptor NP_000116 35 receptor 1 NM_001122740.1 32 I NP_001116212 36 (ESR1) NM_001122741.1 33 NP_001116213 37 NM_001122742.1 34 NP_001116214 38 breast cancer NM_007294.3 39 breast cancer type NP_009225 44 type 1 NM_007300.3 40 1 susceptibility NP_009231.2 45 susceptibility NM_007297.3 41 protein (BRCA1) NP_009228.2 46 protein NM_007298.3 42 NP_009229.2 47 (BRCA1) NM_007299.3 43 NP_009230.2 48 Homo sapiens NM_000059 49 breast cancer type NP_000050 50 breast cancer 2, 2 susceptibility early onset protein (BRCA2) (BRCA2), Androgen NM_000044 51 Androgen NP_000035 53 Receptor (AR) NM_001011645 52 Receptor (AR) NP_001011645 54

In certain embodiments, it is desirable to determine (e.g., assay, measure, approximate) the level (e.g., expression or activity), e.g., one of the above-identified markers. The expression level of such markers may be determined according to any suitable method known in the art. A non-limiting example of such a method includes real-time PCR (RT-PCR), e.g., quantitative RT-PCR (QPCR), which measures the expression level of the mRNA encoding the polypeptide. Real-time PCR evaluates the level of PCR product accumulation during amplification. RNA (or total genomic DNA for detection of germline mutations) is isolated from a sample. RT-PCR can be performed, for example, using a Perkin Elmer/Applied Biosystems (Foster City, Calif.) 7700 Prism instrument. Matching primers and fluorescent probes can be designed for genes of interest using, based on the genes' nucleic acid sequences (e.g., as described above), for example, the primer express program provided by Perkin Elmer/Applied Biosystems (Foster City, Calif.). Optimal concentrations of primers and probes can be initially determined by those of ordinary skill in the art, and control (for example, beta-actin) primers and probes may be obtained commercially from, for example, Perkin Elmer/Applied Biosystems (Foster City, Calif.).

To quantitate the amount of the specific nucleic acid of interest in a sample, a standard curve is generated using a control. Standard curves may be generated using the Ct values determined in the real-time PCR, which are related to the initial concentration of the nucleic acid of interest used in the assay. Standard dilutions ranging from 10-10⁶ copies of the gene of interest are generally sufficient. In addition, a standard curve is generated for the control sequence. This permits standardization of initial content of the nucleic acid of interest in a tissue sample to the amount of control for comparison purposes. Methods of QPCR using TaqMan probes are well known in the art. Detailed protocols for QPCR are provided, for example, for RNA in: Gibson et al., 1996, Genome Res., 10:995-1001; and for DNA in: Heid et al., 1996, Genome Res., 10:986-994; and in Innis et al. (1990) Academic Press, Inc. N.Y.

Expression of mRNA, as well as expression of peptides and other biological factors can also be determined using microarray, methods for which are well known in the art [see, e.g., Watson et al. Curr Opin Biotechnol (1998) 9: 609-14; “DNA microarray technology: Devices, Systems, and Applications” Annual Review of Biomedical Engineering; Vol. 4: 129-153 (2002); Chehab et al. (1989) “Detection of specific DNA sequences by fluorescence amplification: a color complementation assay” Proc. Natl. Acad. Sci. USA, 86: 9178-9182; Lockhart et al. (1996) “Expression monitoring by hybridization to high-density oligonucleotide arrays” Nature Biotechnology, 14: 1675-1680; and M. Schena et al. (1996) “Parallel human genome analysis: Microarray-based expression monitoring of 1000 genes” Proc. Natl. Acad. Sci. USA, 93:10614-10619; Peptide Microarrays Methods and Protocols; Methods in Molecular Biology; Volume 570, 2009, Humana Press; and Small Molecule Microarrays Methods and Protocols; Series: Methods in Molecular Biology, Vol. 669, Uttamchandani, Mahesh; Yao, Shao Q. (Eds.) 2010, 2010, Humana Press]. For example, mRNA expression profiling can be performed to identify differentially expressed genes, wherein the raw intensities determined by microarray are log_(e)-transformed and quantile normalized and gene set enrichment analysis (GSEA) is performed according, e.g., to Subramanian et al. (2005) Proc Natl Acad Sci USA 102:15545-15550).

Other suitable amplification methods include, but are not limited to ligase chain reaction (LCR) (see Wu and Wallace (1989) Genomics 4:560, Landegren et al. (1988) Science 241:1077, and Barringer et al. (1990) Gene 89:117), transcription amplification (Kwoh et al. (1989) Proc. Natl. Acad. Sci. USA 86:1173), self-sustained sequence replication (Guatelli et al. (1990) Proc. Nat. Acad. Sci. USA 87:1874), dot PCR, and linker adapter PCR, etc. In another embodiment, DNA sequencing may be used to determine the presence of ER in a genome. Methods for DNA sequencing are known to those of skill in the art.

Other methods for detecting gene expression (e.g., mRNA levels) include Serial Analysis of Gene Expression applied to high-throughput sequencing (SAGEseq), as described in the present Examples and in Wu Z J et al. Genome Res. 2010 December;20(12):1730-9. 2.

For the detection of germline mutations (e.g., in BRCA1, BRCA2), Southern blotting can also be used. Methods for Southern blotting are known to those of skill in the art (see, e.g., Current Protocols in Molecular Biology, Chapter 19, Ausubel, et al., Eds., Greene Publishing and Wiley-Interscience, New York, 1995, or Sambrook et al., Molecular Cloning: A Laboratory Manual, 2d Ed. vol. 1-3, Cold Spring Harbor Press, NY, 1989). In such an assay, the genomic DNA (typically fragmented and separated on an electrophoretic gel) is hybridized to a probe specific for the target region. Comparison of the intensity of the hybridization signal from the probe for the target region with control probe signal from analysis of normal genomic DNA (e.g., genomic DNA from the same or related cell, tissue, organ, etc.) provides an estimate of the relative copy number of the target nucleic acid. Arrays of nucleic probes can also be employed to detect single or multiple germline or somatic mutations by methods known in the art.

Other examples of suitable methods for detecting expression levels of the cell markers described herein include, e.g., Western blot, ELISA and/or immunohistochemistry, which can be used to measure protein expression level. Such methods are well known in the art.

The frequency of cells that are specific for one or more particular markers (e.g., the frequency of CD44+ or CD24+ breast epithelial cells) can be detected according to any suitable method known in the art. For example, flow cytometry is widely used for analyzing the expression of cell surface and intracellular molecules (on a per cell basis), characterizing and defining different cell types in heterogeneous populations, assessing the purity of isolated subpopulations, and analyzing cell size and volume. This technique is predominantly used to measure fluorescence intensity produced by fluorescent-labeled antibodies or ligands that bind to specific cell-associated molecules, and is described in detail in, e.g., Holmes, K. et al. “Preparation of Cells and Reagents for Flow Cytometry” Current Protocols in Immunology, Unit 5.3.

Non-limiting examples of primary antibodies that may be used to identify the expression of certain markers by one or more assays, e.g., by flow cytometry, immunohistochemistry (IHC), and/or Western blot are listed in Table 2, below:

TABLE 2 Exemplary Cell Marker Primary Antibodies Application (e.g., Cell Western blot, flow Commercial Marker Primary Antibody cytometry, IHC) Source CD24 clone SN3b IHC Neomarkers CD24 clone ML5 FACS Biolegend CD10 56C6 clone IHC Dako CD10 Clone HI10a FACS Biolegend CD44 clone 156-3C11 IHC Neomarkers CD44 Clone 515 FACS BD P27 clone 57/Kip1/p27 IHC Bd Biosciences Ki67 N/A IHC Abcam Sox17 clone 245013 IHC R&D Systems Cox2 clone CX229 IHC Cayman Chemical pEGFR 53A5 (Tyr1173) IHC Cell Signaling Technology Shh Cat# 06-1106 WB, IHC Millipore IGF-1R Clone 24-31 IHC (P) Imgenex pTGFBR Phospho S165 ICC/IF Abcam ER Estrogen Receptor IHC Thermo Scientific (clone SP1) AR Androgen receptor WB/IHC-P/IF/IC/F Cell Signaling (clone D6F11, Technology #5153) BRCA1 MS110 clone IF/IP/WB Calbiochem BRCA2 Cst#CA1033 WB/IP/IHC(P) Millipore Abbreviations: WB: Western blotting; IHC: Immunohistochemistry; IHC-P: immunohistochemistry-paraffin; ICC: immunocytochemistry; IF: immunofluorescence; F: flow cytometry

IV. GENES AND PATHWAYS DIFFERENTIALLY REGULATED BY PARITY STATUS

In certain embodiments, it is desirable to decrease (e.g., inhibit) the expression and/or activity of genes and/or polypeptides encoded by those genes that are discovered herein to be upregulated in breast epithelial cells of nulliparous women relative to parous women. For example, one or more of the genes that are upregulated in CD44+, CD24+, CD10+ and stromal breast epithelial cells of nulliparous women, in Tables 4, 5, 6 and 7, respectively, can be targeted with an inhibitor as described herein in order to treat or prevent breast cancer (e.g., ER+ or ER− breast cancer). Further, for example, one or more of the genes that are upregulated in CD44+ breast epithelial cells of BRCA1 and/or BRCA2 mutation carriers compared to control (normal) breast epithelial cells), as shown, e.g., in Tables 8 and 9 can be targeted with an inhibitor as described herein in order to treat or prevent breast cancer (e.g., ER+ or ER− breast cancer). By way of non-limiting example, asp27 expression is higher in BRCA1 mutation carriers and in BRCA2 mutation carriers compared to control (non-mutation carriers, normal cells), and is an exemplary target for an inhibitor as discussed above.

In other embodiments, it is desirable to increase the expression and/or activity of genes and/or polypeptides encoded by those genes that are discovered herein to be upregulated in breast epithelial cells of parous women relative to nulliparous women. For example, one or more of the genes that are upregulated in CD44+, CD24+, CD10+ and stromal breast epithelial cells of parous women, in Tables 4, 5, 6 and 7, respectively, can be targeted with an agonist as described herein in order to treat or prevent breast cancer (e.g., ER+ or ER− breast cancer). Further, for example, one or more of the genes that are downregulated in CD44+ breast epithelial cells of BRCA1 and/or BRCA2 mutation carriers compared to control (normal) breast epithelial cells), as shown, e.g., in Tables 8 and 9, can be targeted with an agonist as described herein in order to treat or prevent breast cancer (e.g., ER+ or ER− breast cancer).

In certain embodiments, methods for treating breast cancer (e.g., ER+ or ER− breast cancer) involve targeting (e.g., inhibiting) one or more pathways that have increased activity in breast epithelial cells (e.g., CD44+, CD24− breast epithelial cells) of nulliparous women compared to the activity in the breast epithelial cells of parous women (such pathways are also referred to herein as “pathways active in nulliparous (NP) breast epithelial cells”). The identification of such pathways is described in detail in Example 3, below, and the pathways are listed in Tables 10 and 11, below. In a specific embodiment, the pathway is a member selected from the group consisting of cytoskeleton remodeling, chemokine, androgen signaling, cell adhesion, and Wnt signaling. In another embodiment, the pathway includes a mediator molecule selected from the group consisting of cyclic AMP (cAmp) (Signal transduction_cAMP signaling pathway), EGFR (e.g., Development_EGFR signaling via small GTPases pathway, EGFR signaling pathway), Cox2 (e.g., Role and regulation of Prostaglandin E2 in gastric cancer pathway, Hh (e.g., hedgehog signaling pathways), and IGFR (IGFR-IGF signaling pathways).

In other embodiments, methods for treating breast cancer involve targeting (e.g., administering an agonist of) one or more pathways that have decreased activity in breast epithelial cells (e.g., CD44+, CD24− breast epithelial cells) of nulliparous women compared to the breast epithelial cells of parous women (i.e., pathways that have increased activity in breast epithelial cells of parous women, which also referred to herein as “pathways active in parous (P) breast epithelial cells). Such pathways are identified in Example 3 and Tables 10 and 12.

Exemplary pathways are pathways active in nulliparous CD44+, CD24− breast epithelial cells, as shown in Table 11, although pathways active in other nulliparous breast epithelial cells types (e.g., CD24+, CD10+ and/or stromal breast epithelial cells) are also encompassed herein, and include, but are not limited to, Cytoskeleton remodeling_Role of PKA in cytoskeleton reorganisation, Development_MAG-dependent inhibition of neurite outgrowth, Role of DNA methylation in progression of multiple myeloma, Cell adhesion_Histamine H1 receptor signaling in the interruption of cell barrier integrity, Stem cells_Response to hypoxia in glioblastoma stem cells, Development_WNT signaling pathway. Part 2, Development_Slit-Robo signaling, Cytoskeleton remodeling_Fibronectin-binding integrins in cell motility, Oxidative phosphorylation, etc. The genes and the polypeptides encoded by those genes that mediate one or more functions in these pathways are known in the art and can be determined using, e.g., Metaminer software (GeneGo). Thus, the following genes are provided as non-limiting examples of genes involved in the pathways active in nulliparous CD44+, CD24− breast epithelial cells.

For example, genes involved in metabolic pathways active in nulliparous CD44+, CD24− breast epithelial cells (e.g., the pathways: Transcription_Transcription regulation of amino acid metabolism, Regulation of lipid metabolism_Stimulation of Arachidonic acid production by ACM receptors, Ubiquinone metabolism, and Mitochondrial ketone bodies biosynthesis and metabolism), include, but are not limited to, HSD17B11 (GenBank Accession No. BC014327, CA775960), HSD17B12 (GenBank Accession No. AF078850), and HSD17B14 (GenBank Accession No. AF126781), which are involved in regulation of lipid metabolism pathways.

Genes involved in androgen signaling pathways active in nulliparous CD44+, CD24− breast epithelial cells (e.g., the pathways: “Putative role of Estrogen receptor and Androgen receptor signaling in progression of lung cancer”, “Androgen signaling in HCC” (see Tables 10 and 11)) include, but are not limited to, PSA (KLK3) (GenBank Accession Nos. AC011523, BC005307), which are involved in the androgen signaling.

Genes involved in developmental and thyroid signaling pathways active in nulliparous CD44+, CD24− breast epithelial cells (e.g., the pathways: Development_Glucocorticoid receptor signaling, Development_Hedgehog and PTH signaling pathways in bone and cartilage development) include, but are not limited to, NCOR1 (GenBank Accession No. AC002553), NCOR2 (GenBank Accession No. AB209089, AC073916), NCOA4 (GenBank Accession No. AL162047), and NCOA7 (GenBank Accession No. AJ420542).

Genes involved in Wnt signaling pathways active in nulliparous CD44+, CD24-breast epithelial cells (e.g., the pathways: Development_WNT signaling pathway, Cytoskeleton remodeling_TGF, WNT and cytoskeletal remodeling, Stem cells_WNT/Beta-catenin and NOTCH in induction of osteogenesis) include, but are not limited to, SFRP2 (GenBank Accession No. AA449032, AF311912), SFRP4 (GenBank Accession No. AC018634, BT019679), VEGFA (GenBank Accession Nos. AF024710, BF700556), HIF1A (GenBank Accession Nos. BC012527, CN264320), NOTCH1 (GenBank Accession Nos. AB209873, AF308602, AL592301), FN1 (GenBank Accession Nos AI033037, AJ535086).

Genes involved in chemokine pathways active in nulliparous CD44+, CD24-breast epithelial cells (e.g., the pathways: Cell adhesion_Chemokines and adhesion, Cell adhesion_Alpha-4 integrins in cell migration and adhesion, Cell adhesion_Plasmin signaling, Cell adhesion_ECM remodeling, Cell adhesion_Role of tetraspanins in the integrin-mediated cell adhesion) include, but are not limited to, ITGA4 (GenBank Accession No., AC020595) (ITGB1 (GenBank Accession No., AI261443), and TSPAN6 (GenBank Accession Nos. AF043906, BC012389).

Genes involved in cytoskeleton remodeling pathways active in nulliparous CD44+, CD24− breast epithelial cells (e.g., the pathways: Cytoskeleton remodeling_Regulation of actin cytoskeleton by Rho GTPases, Cytoskeleton remodeling_Fibronectin-binding integrins in cell motility, Cytoskeleton remodeling_Reverse signaling by ephrin B, Cytoskeleton remodeling_Role of PKA in cytoskeleton reorganisation) include, but are not limited to, RhoA (GenBank Accession Nos. AK130066, BC000946), RAC1 (GenBank Accession No. AC009412), CDC42 (GenBank Accession No., NM_(—)001039802), and EPHB4 (GenBank Accession Nos. AY056048, BC052804).

The pathways for DNA repair, PI3K/AKT signaling, and apoptosis have been demonstrated herein to be active in parous CD44+, CD24− breast epithelial cells. Other non-limiting examples of pathways active in parous breast epithelial cells include, e.g., TTP metabolism, Resistance of pancreatic cancer cells to death receptor signaling, Transcription_Assembly of RNA Polymerase II preinitiation complex on TATA-less promoters, Development_PIP3 signaling in cardiac myocytes, HCV-dependent regulation of RNA polymerases leading to HCC, Stem cells_H3K9 demethylases in pluripotency maintenance of stem cells, Inhibition of apoptosis in gastric cancer, Cell cycle_Start of DNA replication in early S phase, Apoptosis and survival_Caspase cascade, Immune response_BCR pathway, Immune response_ICOS pathway in T-helper cell, Cell cycle_The metaphase checkpoint, Inhibitory action of Lipoxins on neutrophil migration, Cytoskeleton remodeling_Alpha-1A adrenergic receptor-dependent inhibition of PI3K, DNA damage_NHEJ mechanisms of DSBs repair, Regulation of metabolism_Triiodothyronine and Thyroxine signaling, Cell cycle_Chromosome condensation in prometaphase, Development_IGF-1 receptor signaling, dCTP/dUTP metabolism, dGTP metabolism, Inhibition of RUNX3 signaling in gastric cancer, Apoptosis and survival_Beta-2 adrenergic receptor anti-apoptotic action, Signal transduction_Activin A signaling regulation, Stem cells_Fetal brown fat cell differentiation, Immune response_CXCR4 signaling via second messenger, dATP/dITP metabolism, Signal transduction_PTEN pathway, Microsatellite instability in gastric cancer, Inhibition of TGF-beta signaling in gastric cancer, Immune response_Regulation of T cell function by CTLA-4, DNA damage_DNA-damage-induced responses, etc. (see Tables 10 and 12). The genes and proteins encoded by those genes that mediate functions in these pathways are well known in the art. Thus, the skilled artisan will know which specific genes and/or polypeptides to target (e.g., with an agonist) as described herein (e.g., for the treatment or prevention of breast cancer (e.g., ER+ or ER− breast cancer)).

By way of example, genes involved in apoptosis pathways active in parous CD44+, CD24− breast epithelial cells (e.g., the pathways, Apoptosis and survival_FAS signaling cascades, Apoptosis and survival_Caspase cascade, Apoptosis and survival_HTR1A signaling, Apoptosis and survival_Beta-2 adrenergic receptor anti-apoptotic action, Apoptosis and survival_Granzyme A signaling, Apoptosis and survival_Cytoplasmic/mitochondrial transport of pro-apoptotic proteins Bid, Bmf and Bim) upregulated in parous breast epithelial cells included, but are not limited to, BCL2L11 (GenBank Accession Nos. AC096670, AI268146, AK290377, AY428962), TNFRSF4 (GenBank Accession Nos. AW290885, BC105070), BMPR2 (GenBank Accession Nos. AC009960, BC035097), CASP8 (GenBank Accession Nos. BF439983, AC007256, AF422927), and PP2A (GenBank Accession Nos. AL158151, CD630703, DA052599, X73478).

Genes involve in PI3K/AKT signaling pathways active in parous CD44+, CD24-breast epithelial cells (e.g., the pathways, Cytoskeleton remodeling_Alpha-1A adrenergic receptor-dependent inhibition of PI3K, Signal transduction_AKT signaling, PI3K signaling in gastric cancer) that are upregulated in parous breast epithelial cells included, but are not limited to, PIK3CG (GenBank Accession No. X83368), p85 (GenBank Accession No. AC016564, BC094795, CA427864, CT003423), ILK (GenBank Accession No. BC001554, CB113885, U40282), PDPK1 (GenBank Accession No. AC093525, AC141586, BC012103).

Genes involved in tumor suppressor pathways active in parous breast epithelial cells (e.g., the pathways: Apoptosis and survival_Cytoplasmic/mitochondrial transport of pro-apoptotic proteins Bid, Bmf and Bim, Apoptosis and survival_Caspase cascade, Cytoskeleton remodeling_Alpha-1A adrenergic receptor-dependent inhibition of PI3K, Cell cycle_The metaphase checkpoint) include, but are not limited to, Hakai/CBLL1 (GenBank Accession Nos. AC002467, AK026762, AK293352), CASP8 (GenBank Accession No. BF439983), SCRIB (GenBank Accession No. A1469403), and LLGL2 (GenBank Accession Nos. AC100787, BC031842).

The skilled artisan will appreciate that the foregoing are non-limiting examples of pathways, as well as genes and polypeptides mediating functions in those pathways, that can be targeted (e.g., by an inhibitor or agonist) for the treatment of breast cancer, and other targets, such as those set forth in Tables 10, 11, and 12, below, are also encompassed by the present invention.

V. INHIBITORS AND AGONISTS

Inhibitors and agonists may be used to treat or prevent breast cancer in a subject, as described herein. One of skill in the art will appreciate that the design of such inhibitors and agonists will depend on the specific pathway (e.g., metabolic pathways androgen signaling pathways, tumor suppression, etc., as described above) being targeted. The skilled artisan will understand how to design such inhibitors and agonists, based on methods well known in the art.

The following are thus provided as non-limiting examples (e.g., antisense nucleic acids, RNAi, ribozymes, triple helix forming oligonucleotides (TFOs), antibodies (including, but not limited to intrabodies), aptamers, and other small molecules), and other inhibitors that target pathways (e.g., inhibit expression and/or activity of specific genes and/or polypeptides encoded by those genes that mediate a function in the pathway) that are active in breast epithelial cells of nulliparous women, and agonists that target pathways (e.g., increase expression and/or activity of specific genes and/or polypeptides that mediate a function in the pathway) that are active in parous women, are also encompassed by the present disclosure.

Antisense Nucleic Acids

Antisense oligonucleotides can be used to inhibit the expression of a target polypeptide of the invention (e.g., HSD17B11, HSD17B12, HSD17B14, etc.). Antisense oligonucleotides typically are about 5 nucleotides to about 30 nucleotides in length, about 10 to about 25 nucleotides in length, or about 20 to about 25 nucleotides in length. For a general discussion of antisense technology, see, e.g., Antisense DNA and RNA, (Cold Spring Harbor Laboratory, D. Melton, ed., 1988).

Appropriate chemical modifications of the inhibitors are made to ensure stability of the antisense oligonucleotide, as described below. Changes in the nucleotide sequence and/or in the length of the antisense oligonucleotide can be made to ensure maximum efficiency and thermodynamic stability of the inhibitor. Such sequence and/or length modifications are readily determined by one of ordinary skill in the art.

The antisense oligonucleotides can be DNA or RNA or chimeric mixtures, or derivatives or modified versions thereof, and can be single-stranded or double-stranded. Thus, for example, in the antisense oligonucleotides set forth in herein, when a sequence includes thymidine residues, one or more of the thymidine residues may be replaced by uracil residues and, conversely, when a sequence includes uracil residues, one or more of the uracil residues may be replaced by thymidine residues.

Antisense oligonucleotides comprise sequences complementary to at least a portion of the corresponding target polypeptide. However, 100% sequence complementarity is not required so long as formation of a stable duplex (for single stranded antisense oligonucleotides) or triplex (for double stranded antisense oligonucleotides) can be achieved. The ability to hybridize will depend on both the degree of complementarity and the length of the antisense oligonucleotides. Generally, the longer the antisense oligonucleotide, the more base mismatches with the corresponding nucleic acid target can be tolerated. One skilled in the art can ascertain a tolerable degree of mismatch by use of standard procedures to determine the melting point of the hybridized complex.

Antisense nucleic acid molecules can be encoded by a recombinant gene for expression in a cell (see, e.g., U.S. Pat. Nos. 5,814,500 and 5,811,234), or alternatively they can be prepared synthetically (see, e.g., U.S. Pat. No. 5,780,607).

The antisense oligonucleotides can be modified at the base moiety, sugar moiety, or phosphate backbone, or a combination thereof. In one embodiment, the antisense oligonucleotide comprises at least one modified sugar moiety, e.g., a sugar moiety such as arabinose, 2-fluoroarabinose, xylulose, and hexose.

In another embodiment, the antisense oligonucleotide comprises at least one modified phosphate backbone such as a phosphorothioate, a phosphorodithioate, a phosphoramidothioate, a phosphoramidate, a phosphordiamidate, a methylphosphonate, an alkyl phosphotriester, and a formacetal or analog thereof. Examples include, without limitation, phosphorothioate antisense oligonucleotides (e.g., an antisense oligonucleotide phosphothioate modified at 3′ and 5′ ends to increase its stability) and chimeras between methylphosphonate and phosphodiester oligonucleotides. These oligonucleotides provide good in vivo activity due to solubility, nuclease resistance, good cellular uptake, ability to activate RNase H, and high sequence selectivity.

Other examples of synthetic antisense oligonucleotides include oligonucleotides that contain phosphorothioates, phosphotriesters, methyl phosphonates, short chain alkyl, or cycloalkyl intersugar linkages or short chain heteroatomic or heterocyclic intersugar linkages. Most preferred are those with CH2-NH—O—CH2, CH2-N(CH3)-O—CH2, CH2-O—N(CH3)-CH2, CH2-N(CH3)-N(CH3)-CH2 and O—N(CH3)-CH2-CH2 backbones (where phosphodiester is O—PO2-O—CH2). U.S. Pat. No. 5,677,437 describes heteroaromatic oligonucleoside linkages. Nitrogen linkers or groups containing nitrogen can also be used to prepare oligonucleotide mimics (U.S. Pat. Nos. 5,792,844 and 5,783,682). U.S. Pat. No. 5,637,684 describes phosphoramidate and phosphorothioamidate oligomeric compounds.

In other embodiments, such as the peptide-nucleic acid (PNA) backbone, the phosphodiester backbone of the oligonucleotide may be replaced with a polyamide backbone, the bases being bound directly or indirectly to the aza nitrogen atoms of the polyamide backbone (Nielsen et al., Science 1991; 254:1497). Other synthetic oligonucleotides may contain substituted sugar moieties comprising one of the following at the 2′ position: OH, SH, SCH3, F, OCN, O(CH2)nNH2 or O(CH2)nCH3 where n is from 1 to about 10; C1 to C10 lower alkyl, substituted lower alkyl, alkaryl or aralkyl; Cl; Br; CN; CF3; OCF3; O—; S-, or N-alkyl; O-, S-, or N-alkenyl; SOCH3; SO2CH3; ONO2; NO2; N3; NH2; heterocycloalkyl; heterocycloalkaryl; aminoalkylamino; polyalkylamino; substituted sialyl; a fluorescein moiety; an RNA cleaving group; a reporter group; an intercalator; a group for improving the pharmacokinetic properties of an oligonucleotide; or a group for improving the pharmacodynamic properties of an oligonucleotide, and other substituents having similar properties.

Oligonucleotides may also have sugar mimetics such as cyclobutyls or other carbocyclics in place of the pentofuranosyl group. Nucleotide units having nucleosides other than adenosine, cytidine, guanosine, thymidine and uridine may be used, such as inosine. In other embodiments, locked nucleic acids (LNA) can be used (reviewed in, e.g., Jepsen and Wengel, Curr. Opin. Drug Discov. Devel. 2004; 7:188-194; Crinelli et al., Curr. Drug Targets 2004; 5:745-752). LNA are nucleic acid analog(s) with a 2′-O, 4′-C methylene bridge. This bridge restricts the flexibility of the ribofuranose ring and locks the structure into a rigid C3-endo conformation, conferring enhanced hybridization performance and exceptional biostability. LNA allows the use of very short oligonucleotides (less than 10 bp) for efficient hybridization in vivo.

In one embodiment, an antisense oligonucleotide can comprise at least one modified base moiety such as a group including but not limited to 5-fluorouracil, 5-bromouracil, 5-chlorouracil, 5-iodouracil, hypoxanthine, xantine, 4-acetylcytosine, 5-(carboxyhydroxylmethyl) uracil, 5-carboxymethylaminomethyl-2-thiouridine, 5-carboxymethylaminomethyluracil, dihydrouracil, beta-D-galactosylqueosine, inosine, N6-isopentenyladenine, 1-methylguanine, 1-methylinosine, 2,2-dimethylguanine, 2-methyladenine, 2-methylguanine, 3-methylcytosine, 5-methylcytosine, N6-adenine, 7-methylguanine, 5-methylaminomethyluracil, 5-methoxyaminomethyl-2-thiouracil, beta-D-mannosylqueosine, 5-methoxycarboxymethyluracil, 5-methoxyuracil, 2-methylthio-N6-isopentenyladenine, uracil-5-oxyacetic acid (v), pseudouracil, queosine, 2-thiocytosine, 5-methyl-2-thiouracil, 2-thiouracil, 4-thiouracil, 5-methyluracil, uracil-5-oxyacetic acid methylester, uracil-5-oxyacetic acid (v), 5-methyl-2-thiouracil, 3-(3-amino-3-N-2-carboxypropyl) uracil, (acp3)w, and 2,6-diaminopurine.

In another embodiment, the antisense oligonucleotide can include α-anomeric oligonucleotides. An α-anomeric oligonucleotide forms specific double-stranded hybrids with complementary RNA in which, contrary to the usual β-units, the strands run parallel to each other (Gautier et al., Nucl. Acids Res. 1987; 15:6625-6641).

Oligonucleotides may have morpholino backbone structures (U.S. Pat. No. 5,034,506). Thus, in yet another embodiment, the antisense oligonucleotide can be a morpholino antisense oligonucleotide (i.e., an oligonucleotide in which the bases are linked to 6-membered morpholine rings, which are connected to other morpholine-linked bases via non-ionic phosphorodiamidate intersubunit linkages). Morpholino oligonucleotides are highly resistant to nucleases and have good targeting predictability, high in-cell efficacy and high sequence specificity (U.S. Pat. No. 5,034,506; Summerton, Biochim. Biophys. Acta 1999; 1489:141-158; Summerton and Weller, Antisense Nucleic Acid Drug Dev. 1997; 7:187-195; Arora et al., J. Pharmacol. Exp. Ther. 2000; 292:921-928; Qin et al., Antisense Nucleic Acid Drug Dev. 2000; 10:11-16; Heasman et al., Dev. Biol. 2000; 222:124-134; Nasevicius and Ekker, Nat. Genet. 2000; 26:216-220).

Antisense oligonucleotides may be chemically synthesized, for example using appropriately protected ribonucleoside phosphoramidites and a conventional DNA/RNA synthesizer. Antisense nucleic acid oligonucleotides can also be produced intracellularly by transcription from an exogenous sequence. For example, a vector can be introduced in vivo such that it is taken up by a cell within which the vector or a portion thereof is transcribed to produce an antisense RNA. Such a vector can remain episomal or become chromosomally integrated, so long as it can be transcribed to produce the desired antisense RNA. Such vectors can be constructed by recombinant DNA technology methods standard in the art. Vectors can be plasmid, viral, or others known in the art, used for replication and expression in mammalian cells. In another embodiment, “naked” antisense nucleic acids can be delivered to adherent cells via “scrape delivery”, whereby the antisense oligonucleotide is added to a culture of adherent cells in a culture vessel, the cells are scraped from the walls of the culture vessel, and the scraped cells are transferred to another plate where they are allowed to re-adhere. Scraping the cells from the culture vessel walls serves to pull adhesion plaques from the cell membrane, generating small holes that allow the antisense oligonucleotides to enter the cytosol.

RNAi

Reversible short inhibition of a target polypeptide (e.g., Gfpt1, RPIA, RPE, etc.) of the invention may also be useful. Such inhibition can be achieved by use of siRNAs. RNA interference (RNAi) technology prevents the expression of genes by using small RNA molecules such as small interfering RNAs (siRNAs). This technology in turn takes advantage of the fact that RNAi is a natural biological mechanism for silencing genes in most cells of many living organisms, from plants to insects to mammals (McManus et al., Nature Reviews Genetics, 2002, 3(10) p. 737). RNAi prevents a gene from producing a functional protein by ensuring that the molecule intermediate, the messenger RNA copy of the gene is destroyed siRNAs can be used in a naked form and incorporated in a vector, as described below.

RNA interference (RNAi) is a process of sequence-specific post-transcriptional gene silencing by which double stranded RNA (dsRNA) homologous to a target locus can specifically inactivate gene function in plants, fungi, invertebrates, and vertebrates, including mammals (Hammond et al., Nature Genet. 2001; 2:110-119; Sharp, Genes Dev. 1999; 13:139-141). This dsRNA-induced gene silencing is mediated by short double-stranded small interfering RNAs (siRNAs) generated from longer dsRNAs by ribonuclease III cleavage (Bernstein et al., Nature 2001; 409:363-366 and Elbashir et al., Genes Dev. 2001; 15:188-200). RNAi-mediated gene silencing is thought to occur via sequence-specific RNA degradation, where sequence specificity is determined by the interaction of an siRNA with its complementary sequence within a target RNA (see, e.g., Tuschl, Chem. Biochem. 2001; 2:239-245).

For mammalian systems, RNAi commonly involves the use of dsRNAs that are greater than 500 bp; however, it can also be activated by introduction of either siRNAs (Elbashir, et al., Nature 2001; 411: 494-498) or short hairpin RNAs (shRNAs) bearing a fold back stem-loop structure (Paddison et al., Genes Dev. 2002; 16: 948-958; Sui et al., Proc. Natl. Acad. Sci. USA 2002; 99:5515-5520; Brummelkamp et al., Science 2002; 296:550-553; Paul et al., Nature Biotechnol. 2002; 20:505-508).

The siRNAs are preferably short double stranded nucleic acid duplexes comprising annealed complementary single stranded nucleic acid molecules. Preferably, the siRNAs are short dsRNAs comprising annealed complementary single strand RNAs. siRNAs may also comprise an annealed RNA:DNA duplex, wherein the sense strand of the duplex is a DNA molecule and the antisense strand of the duplex is a RNA molecule.

Preferably, each single stranded nucleic acid molecule of the siRNA duplex is of from about 19 nucleotides to about 27 nucleotides in length. In preferred embodiments, duplexed siRNAs have a 2 or 3 nucleotide 3′ overhang on each strand of the duplex. In preferred embodiments, siRNAs have 5′-phosphate and 3′-hydroxyl groups.

RNAi molecules may include one or more modifications, either to the phosphate-sugar backbone or to the nucleoside. For example, the phosphodiester linkages of natural RNA may be modified to include at least one heteroatom other than oxygen, such as nitrogen or sulfur. In this case, for example, the phosphodiester linkage may be replaced by a phosphothioester linkage. Similarly, bases may be modified to block the activity of adenosine deaminase. Where the RNAi molecule is produced synthetically, or by in vitro transcription, a modified ribonucleoside may be introduced during synthesis or transcription. The skilled artisan will understand that many of the modifications described above for antisense oligonucleotides may also be made to RNAi molecules. Such modifications are well known in the art.

siRNAs may be introduced to a target cell as an annealed duplex siRNA, or as single stranded sense and antisense nucleic acid sequences that, once within the target cell, anneal to form the siRNA duplex. Alternatively, the sense and antisense strands of the siRNA may be encoded on an expression construct that is introduced to the target cell. Upon expression within the target cell, the transcribed sense and antisense strands may anneal to reconstitute the siRNA.

shRNAs typically comprise a single stranded “loop” region connecting complementary inverted repeat sequences that anneal to form a double stranded “stem” region. Structural considerations for shRNA design are discussed, for example, in McManus et al., RNA 2002; 8:842-850. In certain embodiments the shRNA may be a portion of a larger RNA molecule, e.g., as part of a larger RNA that also contains U6 RNA sequences (Paul et al., supra).

In preferred embodiments, the loop of the shRNA is from about 1 to about 9 nucleotides in length. In preferred embodiments the double stranded stem of the shRNA is from about 19 to about 33 base pairs in length. In preferred embodiments, the 3′ end of the shRNA stem has a 3′ overhang. In particularly preferred embodiments, the 3′ overhang of the shRNA stem is from 1 to about 4 nucleotides in length. In preferred embodiments, shRNAs have 5′-phosphate and 3′-hydroxyl groups.

Although RNAi molecules preferably contain nucleotide sequences that are fully complementary to a portion of the target nucleic acid, 100% sequence complementarity between the RNAi probe and the target nucleic acid is not required.

Similar to the above-described antisense oligonucleotides, RNAi molecules can be synthesized by standard methods known in the art, e.g., by use of an automated synthesizer. RNAs produced by such methodologies tend to be highly pure and to anneal efficiently to form siRNA duplexes or shRNA hairpin stem-loop structures. Following chemical synthesis, single stranded RNA molecules are deprotected, annealed to form siRNAs or shRNAs, and purified (e.g., by gel electrophoresis or HPLC). Alternatively, standard procedures may be used for in vitro transcription of RNA from DNA templates carrying RNA polymerase promoter sequences (e.g., T7 or SP6 RNA polymerase promoter sequences). Efficient in vitro protocols for preparation of siRNAs using T7 RNA polymerase have been described (Done and Picard, Nucleic Acids Res. 2002; 30:e46; and Yu et al., Proc. Natl. Acad. Sci. USA 2002; 99:6047-6052). Similarly, an efficient in vitro protocol for preparation of shRNAs using T7 RNA polymerase has been described (Yu et al., supra). The sense and antisense transcripts may be synthesized in two independent reactions and annealed later, or may be synthesized simultaneously in a single reaction.

RNAi molecules may be formed within a cell by transcription of RNA from an expression construct introduced into the cell. For example, both a protocol and an expression construct for in vivo expression of siRNAs are described in Yu et al., supra. The delivery of siRNA to tumors can potentially be achieved via any of several gene delivery “vehicles” that are currently available. These include viral vectors, such as adenovirus, lentivirus, herpes simplex virus, vaccinia virus, and retrovirus, as well as chemical-mediated gene delivery systems (for example, liposomes), or mechanical DNA delivery systems (DNA guns). The oligonucleotides to be expressed for such siRNA-mediated inhibition of gene expression would be between 18 and 28 nucleotides in length. Protocols and expression constructs for in vivo expression of shRNAs have been described (Brummelkamp et al., Science 2002; 296:550-553; Sui et al., supra; Yu et al., supra; McManus et al., supra; Paul et al., supra).

The expression constructs for in vivo production of RNAi molecules comprise RNAi encoding sequences operably linked to elements necessary for the proper transcription of the RNAi encoding sequence(s), including promoter elements and transcription termination signals. Preferred promoters for use in such expression constructs include the polymerase-III HI-RNA promoter (see, e.g., Brummelkamp et al., supra) and the U6 polymerase-III promoter (see, e.g., Sui et al., supra; Paul, et al. supra; and Yu et al., supra). The RNAi expression constructs can further comprise vector sequences that facilitate the cloning of the expression constructs. Standard vectors are known in the art (e.g., pSilencer 2.0-U6 vector, Ambion Inc., Austin, Tex.).

Ribozyme Inhibition

The level of expression of a target polypeptide of the invention can also be inhibited by ribozymes designed based on the nucleotide sequence thereof.

Ribozymes are enzymatic RNA molecules capable of catalyzing the sequence-specific cleavage of RNA (for a review, see Rossi, Current Biology 1994; 4:469-471). The mechanism of ribozyme action involves sequence-specific hybridization of the ribozyme molecule to complementary target RNA, followed by an endonucleolytic cleavage event. The composition of ribozyme molecules must include: (i) one or more sequences complementary to the target RNA; and (ii) a catalytic sequence responsible for RNA cleavage (see, e.g., U.S. Pat. No. 5,093,246).

The use of hammerhead ribozymes is preferred. Hammerhead ribozymes cleave RNAs at locations dictated by flanking regions that form complementary base pairs with the target RNA. The sole requirement is that the target RNA has the following sequence of two bases: 5′-UG-3′. The construction of hammerhead ribozymes is known in the art, and described more fully in Myers, Molecular Biology and Biotechnology: A Comprehensive Desk Reference, VCH Publishers, New York, 1995 (see especially FIG. 4, page 833) and in Haseloff and Gerlach, Nature 1988; 334:585-591.

As in the case of antisense oligonucleotides, ribozymes can be composed of modified oligonucleotides (e.g., for improved stability, targeting, etc.). These can be delivered to cells which express the target polypeptide in vivo. A preferred method of delivery involves using a DNA construct “encoding” the ribozyme under the control of a strong constitutive pol III or pol II promoter, so that transfected cells will produce sufficient quantities of the ribozyme to catalyze cleavage of the target mRNA encoding the target polypeptide. However, because ribozymes, unlike antisense molecules, are catalytic, a lower intracellular concentration may be required to achieve an adequate level of efficacy.

Ribozymes can be prepared by any method known in the art for the synthesis of DNA and RNA molecules, as discussed above. Ribozyme technology is described further in Intracellular Ribozyme Applications: Principals and Protocols, Rossi and Couture eds., Horizon Scientific Press, 1999.

Triple Helix Forming Oligonucleotides (TFOs)

Nucleic acid molecules useful to inhibit expression level of a target polypeptide of the invention via triple helix formation are preferably composed of deoxynucleotides. The base composition of these oligonucleotides is typically designed to promote triple helix formation via Hoogsteen base pairing rules, which generally require sizeable stretches of either purines or pyrimidines to be present on one strand of a duplex. Nucleotide sequences may be pyrimidine-based, resulting in TAT and CGC triplets across the three associated strands of the resulting triple helix. The pyrimidine-rich molecules provide base complementarity to a purine-rich region of a single strand of the duplex in a parallel orientation to that strand. In addition, nucleic acid molecules may be chosen that are purine-rich, e.g., those containing a stretch of G residues. These molecules will form a triple helix with a DNA duplex that is rich in GC pairs, in which the majority of the purine residues are located on a single strand of the targeted duplex, resulting in GGC triplets across the three strands in the triplex.

Alternatively, sequences can be targeted for triple helix formation by creating a so-called “switchback” nucleic acid molecule. Switchback molecules are synthesized in an alternating 5′-3′, 3′-5′ manner, such that they base pair with first one strand of a duplex and then the other, eliminating the necessity for a sizeable stretch of either purines or pyrimidines to be present on one strand of a duplex.

Similarly to RNAi molecules, antisense oligonucleotides, and ribozymes, described above, triple helix molecules can be prepared by any method known in the art. These include techniques for chemically synthesizing oligodeoxyribonucleotides and oligoribonucleotides such as, e.g., solid phase phosphoramidite chemical synthesis. Alternatively, RNA molecules can be generated by in vitro or in vivo transcription of DNA sequences “encoding” the particular RNA molecule. Such DNA sequences can be incorporated into a wide variety of vectors that incorporate suitable RNA polymerase promoters such as the T7 or SP6 polymerase promoters. See, Nielsen, P. E. “Triple Helix: Designing a New Molecule of Life”, Scientific American, December, 2008; Egholm, M., et al. “PNA Hybridizes to Complementary Oligonucleotides Obeying the Watson-Crick Hydrogen Bonding Rules.” (1993) Nature, 365, 566-568; Nielsen, P. E. ‘PNA Technology’. Mol Biotechnol. 2004; 26:233-48.

Antibodies and Aptamers

The polypeptide targets described herein, e.g., HSD17B11, HSD17B12, HSD17B14, etc.) can be inhibited (e.g., the level can be reduced) by the administration to or expression in a subject or a cell or tissue thereof, of blocking antibodies or aptamers against the polypeptide.

Antibodies, or their equivalents and derivatives, e.g., intrabodies, or other antagonists of the polypeptide, may be used in accordance with the present methods. Methods for engineering intrabodies (intracellular single chain antibodies) are well known. Intrabodies are specifically targeted to a particular compartment within the cell, providing control over where the inhibitory activity of the treatment is focused. This technology has been successfully applied in the art (for review, see Richardson and Marasco, 1995, TIBTECH vol. 13; Lo et al. (2009) Handb Exp Pharmacol. 181:343-73; Maraasco, W. A. (1997) Gene Therapy 4:11-15; see also, U.S. Pat. Appln. Pub. No. 2001/0024831 by Der Maur et al. and U.S. Pat. No. 6,004,940 by Marasco et al.).

Administration of a suitable dose of the antibody or the antagonist (e.g., aptamer) may serve to block the level (expression or activity) of the polypeptide in order to treat or prevent cancer, e.g., inhibit growth of a breast cancer cell or tumor (e.g., ER+ or ER− breast cancer cell or tumor).

In addition to using antibodies and aptamers to inhibit the levels and/or activity of a target polypeptide, it may also be possible to use other forms of inhibitors. For example, it may be possible to identify antagonists that functionally inhibit the target polypeptide (e.g., HSD17B11, HSD17B12, HSD17B14, etc.). In addition, it may also be possible to interfere with the interaction of the polypeptide with its substrate. Other suitable inhibitors will be apparent to the skilled person.

The antibody (or other inhibitors and antagonists) can be administered by a number of methods. For example, for the administration of intrabodies, one method is set forth by Marasco and Haseltine in PCT WO 94/02610. This method discloses the intracellular delivery of a gene encoding the intrabody. In one embodiment, a gene encoding a single chain antibody is used. In another embodiment, the antibody would contain a nuclear localization sequence. By this method, one can intracellularly express an antibody, which can block activity of the target polypeptide in desired cells.

Aptamers are oligonucleic acid or peptide molecules that bind to a specific target molecule. Aptamers can be used to inhibit gene expression and to interfere with protein interactions and activity. Nucleic acid aptamers are nucleic acid species that have been engineered through repeated rounds of in vitro selection (e.g., by SELEX (systematic evolution of ligands by exponential enrichment)) to bind to various molecular targets such as small molecules, proteins, nucleic acids, and even cells, tissues and organisms. Peptide aptamers consist of a variable peptide loop attached at both ends to a protamersein scaffold. Aptamers are useful in biotechnological and therapeutic applications as they offer molecular recognition properties that rival that of antibodies. Aptamers can be engineered completely in a test tube, are readily produced by chemical synthesis, possess desirable storage properties, and elicit little or no immunogenicity in therapeutic application. Aptamers can be produced using the methodology disclosed in a U.S. Pat. No. 5,270,163 and WO 91/19813.

Small Molecules

Chemical agents, referred to in the art as “small molecule” compounds are typically organic, non-peptide molecules, having a molecular weight less than 10,000 Da, preferably less than 5,000 Da, more preferably less than 1,000 Da, and most preferably less than 500 Da. This class of modulators includes chemically synthesized molecules, for instance, compounds from combinatorial chemical libraries. Synthetic compounds may be rationally designed or identified utilizing the screening methods described below. Methods for generating and obtaining small molecules are well known in the art (Schreiber, Science 2000; 151:1964-1969; Radmann et al., Science 2000; 151:1947-1948).

Non-limiting of small molecule inhibitors (and exemplary dosages for in vitro use in cell-based assays) include, e.g., cyclopamine (e.g., 10 μM) (Selleck Chemicals, cat#S1146), an inhibitor of Smo receptor of Hh ligands; LY2109761 (e.g., 500 nM) (Eli Lilly), an inhibitor of TGFBR kinases; celecoxib (e.g., 100 μM) (LKT laboratories, cat#C1644), an inhibitor of Cox2; 2-5dideoxyadenosine (e.g., 100 μM) (Enzo Life Sciences, cat#BML-CN110-005), an inhibitor of adenylate cyclase; tyrphostin AG1478 (e.g., 10 μM) (Cayman Chemicals, cat#10010244), an inhibitor of EGFR; XAV939 (e.g., 1 μM)(Tocris Bioscience, cat#3748), a Tankyrase (TNKS) inhibitor that antagonizes Wnt signaling via stimulation of β-catenin degradation and stabilization of axin; and picropodophylotoxin (e.g., 0.5 μM) (Tocris Bioscience, cat#2956), an IGFR inhibitor in which stock solutions (1,000×) are prepared in DMSO.

Non-limiting examples of small molecule agonists include, e.g., the TFGb agonists described in detail in U.S. Pat. No. 8,097,645 to Wyss-Coray et al., the hedgehog (Hh) agonist cyclopamine (see, King, W K. Journal of Biology 2002, 1:8); the Wnt agonist Calbiochem (EMD Millipore), and the cAMP agonist Alotaketal A described in Huang et al. (J. Am. Chem. Soc., 2012, 134 (21), pp 8806-8809).

In certain embodiments, the above described inhibitors and agonists can be directly targeted to a specific cell type (e.g., CD44+ or CD24+ breast epithelial cells, p27+ or Ki67+ breast epithelial cells, AR+ cells (e.g., AR+ breast epithelial cells), ER+ breast epithelial cells, ER− breast epithelial cells, and combinations thereof, e.g., ER+p27+ cells (e.g., ER+p27+ breast epithelial cells), or AR+p27+ cells (e.g., AR+p27+ breast epithelial cells), etc. The skilled artisan will appreciate that methods for specific cell targeting are well known in the art. By way of non-limiting example, antibodies, e.g., an anti-CD44, anti-CD24, anti-AR, or anti-ER antibody, etc., may be conjugated to an inhibitor or agonist described herein, in order to target the inhibitor or agonist to, for example and without limitation, CD44+, CD24+ or ER+ cells. Further the site of administration (e.g., direct injection into breast tissue and/or breast tumor) can further increase the specificity of cell targeting.

VI. METHODS FOR PREDICTING A SUBJECT'S RISK OF DEVELOPING BREAST CANCER

Provided herein are methods for predicting a subject's risk of developing breast cancer (e.g., ER+ or ER− breast cancer).

In one embodiment, the method comprises (a) determining the frequency in a breast tissue sample of CD44+, CD24− breast epithelial cells and (b) predicting that the subject has a relatively elevated risk of developing breast cancer if the frequency of CD44+, CD24− breast epithelial cells is decreased compared to a first control frequency of CD44+, CD24− breast epithelial cells; or (c) predicting that the subject has a relatively reduced risk of developing breast cancer if the frequency of CD44+ breast epithelial cells is increased compared to a second control frequency of CD44+, CD24− breast epithelial cells.

In another embodiment, the method comprises: (a) determining the frequency in a breast tissue sample of CD24+ breast epithelial cells and (b) predicting that the subject has a relatively elevated risk of developing breast cancer if the frequency of CD24+ breast epithelial cells is increased compared to a first control frequency of CD24+ breast epithelial cells; or (c) predicting that the subject has a relatively reduced risk of developing breast cancer if the frequency of CD24+ breast epithelial cells is decreased compared to a second control frequency of CD24+ breast epithelial cells.

As discussed in the Definitions section, above, a “first control frequency” of a cell type (e.g., CD44+ or CD24+ cells, or p27+ cells, Ki67+ cells, etc.) is the frequency of that cell type in a comparable sample from a patient or the average frequency in comparable samples from a plurality of patients known to be at low risk of developing breast cancer (e.g., parous women not expressing BRCA1 or BRCA2 mutations, where the women are premenopausal and/or postmenopausal). In other words, the first control frequency is a “negative” control for an elevated risk of developing breast cancer. As also discussed above, a “second control frequency” of a cell type is the frequency of that cell type in a comparable sample from a patient or the average frequency in comparable samples from a plurality of patients known to be at high risk of developing breast cancer (e.g., pre and/or postmenopausal nulliparous women). In other words, the second control frequency is a “positive” control for an elevated risk of developing the breast cancer. The first and second control frequencies can be simultaneously determined or can be determined before or after the frequency of the relevant cell is determined in the breast cells from the subject for whom the risk prediction is being made.

In a particularly preferred embodiment, the frequency of both CD44+ and CD24+ breast epithelial cells in the sample is determined as described above, and the method comprises predicting that the subject has a relatively elevated risk of developing breast cancer if: (i) the frequency of CD44+, CD24− breast epithelial cells is decreased compared to a first control frequency of CD44+, CD24− breast epithelial cells, and (ii) the frequency of CD24+ breast epithelial cells is increased compared to a first control frequency of CD24+ breast epithelial cells; and step (c) comprises predicting that the subject has a relatively reduced risk of developing breast cancer if: (i) the frequency of CD44+ breast epithelial cells is increased compared to a second control frequency of CD44+, CD24− breast epithelial cells, and (ii) the frequency of CD24+ breast epithelial cells is decreased compared to a second control frequency of CD24+ breast epithelial cells.

In other embodiments, the first and second control frequencies of CD44+ and CD24+ breast epithelial cells, described above, can also be first and second predetermined reference frequencies, respectively (i.e., standards) to which the frequency of the cell type in a test sample is compared.

For example, the predetermined reference frequency for a first control frequency, of CD44+, CD24− breast epithelial cells is preferably in the range of 15-30% or higher of the total breast epithelial cells in the sample. Further, as disclosed herein, a subject considered to have a relatively elevated risk of developing breast cancer will have a decreased frequency of CD44+, CD24− breast epithelial cells relative to that predetermined reference frequency; thus, a subject determined to have a frequency of CD44+, CD24− breast epithelial cells less than 15% would be predicted to have a relatively elevated risk of developing breast cancer. More preferably, a subject determined to have a frequency of CD44+, CD24− breast epithelial cells less than 14%, less than 13%, less than 12%, less than 11%, less than 10%, less than 9%, less than 8%, less than 7%, less than 6%, or less than 5%, is predicted to have a relatively elevated risk of developing breast cancer.

The predetermined reference frequency for a second control frequency of CD44+, CD24− breast epithelial cells is preferably in the range of 15% or less (e.g., less than 15%, less than 14%, less than 13%, less than 12%, less than 11%, less than 10%, etc.) of the total breast epithelial cells in the sample. As disclosed herein, a subject considered to have a relatively reduced risk of developing breast cancer will have an increased frequency of CD44+, CD24-breast epithelial cells relative to the second predetermined reference frequency; thus, a subject determined to have a frequency of CD44+, CD24− breast epithelial cells greater than 15%, preferably greater than 16%, greater than 17%, greater than 18%, greater than 19%, greater than 20%, greater than 21%, greater than 22%, greater than 23%, greater than 24%, greater than 25%, greater than 26%, greater than 27%, greater than 28%, greater than 29%, or greater than 30% is predicted to have a relatively reduced risk of developing breast cancer.

The first predetermined reference frequency of CD24+ breast epithelial cells is preferably 20%, or less than 20%, less than 19%, less than 18%, less than 17%, less than 16%, less than 15%, less than 14%, less than 13%, less than 12%, less than 11%, less than 10%, less than 9%, less than 8%, less than 7%, less than 6%, or less than 5% of the total breast epithelial cells in the sample. As disclosed herein, a subject considered to have a relatively elevated risk of developing breast cancer will have an increased frequency of CD24+ breast epithelial cells relative to the first predetermined reference frequency of CD24+ breast epithelial cells; thus, a subject determined to have a frequency of CD24+ breast epithelial cells greater than 20%, greater than 21%, greater than 22%, greater than 23%, greater than 24%, greater than 25%, greater than 26%, greater than 27%, greater than 28%, greater than 29%, greater than 30%, greater than 31%, greater than 32%, greater than 33%, greater than 34%, greater than 35%, greater than 36%, greater than 37%, greater than 38%, greater than 39%, greater than 40%, greater than 41%, greater than 42%, greater than 43%, greater than 44%, greater than 45%, greater than 46%, greater than 47%, greater than 48%, greater than 49%, or greater than 50% of the total breast epithelial cells in the sample, is predicted to have a relatively elevated risk of developing breast cancer.

The second predetermined reference frequency of CD24+ breast epithelial cells is preferably 20%, or greater than 20%, greater than 21%, greater than 22%, greater than 23%, greater than 24%, greater than 25%, greater than 26%, greater than 27%, greater than 28%, greater than 29%, greater than 30%, greater than 31%, greater than 32%, greater than 33%, greater than 34%, greater than 35%, greater than 36%, greater than 37%, greater than 38%, greater than 39%, greater than 40%, greater than 41%, greater than 42%, greater than 43%, greater than 44%, greater than 45%, greater than 46%, greater than 47%, greater than 48%, greater than 49%, or greater than 50%, of the total breast epithelial cells in the sample. As disclosed herein, a subject considered to have a relatively reduced risk of developing breast cancer will have a decreased frequency of CD24+ breast epithelial cells relative to the second predetermined reference frequency; thus, a subject determined to have a frequency of CD24+ breast epithelial cells less than 20% (e.g., less than 20%, less than 19%, less than 18%, less than 17%, less than 16%, less than 15%, less than 14%, less than 13%, less than 12%, less than 11%, less than 10%, less than 5%, etc.) would be predicted to have a relatively reduced risk of developing breast cancer.

In yet other embodiments, the method for predicting a subject's risk of developing an breast cancer comprises: predicting that the subject has a relatively elevated risk of developing breast cancer if the frequency of CD24+ breast epithelial cells is greater than the frequency of CD44+, CD24− breast epithelial cells in the sample; and step (c) comprises predicting that the subject has a relatively reduced risk of developing breast cancer if the frequency of CD24+ breast epithelial cells is equal to or less than the frequency of CD44+, CD24− breast epithelial cells in the sample. In still other embodiments, the method for predicting a subject's risk of developing an breast cancer comprises predicting that the subject has a relatively elevated risk of developing breast cancer if the ratio of CD24+ breast epithelial cells to CD44+, CD24− breast epithelial cells in a breast epithelial cell-containing sample from the subject is 2, or greater than 2, greater than 3, greater than 4, greater than 5, greater than 6, greater than 7, greater than 8, greater than 9, or greater than 10; or, predicting that the subject has a relatively reduced risk of developing breast cancer if the ratio of CD24+ breast epithelial cells to CD44+, CD24− breast epithelial cells in a breast epithelial cell-containing sample from the subject is less than 2, preferably less than 1.5, less than 1, less than 0.9, less than 0.8, less than 0.7, less than 0.6, less than 0.5, less than 0.4, less than 0.3, less than 0.2, less than 0.1, less than 0.05, or less than 0.01.

In other embodiments, a method of predicting a subject's risk of developing an estrogen-receptor-positive (ER+) breast cancer is provided, wherein the method comprises: (a) determining the frequency in a breast tissue sample of cells of one or more types of cells, such as, e.g., p27+ breast epithelial cells, Sox17+ breast epithelial cells, Cox2+ breast epithelial cells, Ki67+ breast epithelial cells, ER+, p27+ breast epithelial cells, ER+, Sox17+ breast epithelial cells, ER+, Cox2+ breast epithelial cells, ER+, Ki67+ breast epithelial cells, AR+, p27+ breast epithelial cells, AR+, Sox17+ breast epithelial cells, AR+, Cox2+ breast epithelial cells, and AR+, Ki67+ breast epithelial cells; and (b) predicting that the subject has a relatively elevated risk of developing breast cancer if the frequency of the cells of the type is increased compared to a first control frequency of cells of the type; or (c) predicting that the subject has a relatively reduced risk of developing breast cancer if the frequency of the cells of the type is decreased compared to a second control frequency of the cells of the type. In a preferred embodiment, the frequencies of two or more, three or more, or all of the cell types (e.g., p27+, Ki67+, Sox17 and/or Cox2+ breast epithelial cells and/or ER+, p27+ breast epithelial cells, ER+, Sox17+ breast epithelial cells, ER+, Cox2+ breast epithelial cells, ER+, Ki67+ breast epithelial cells, AR+, p27+ breast epithelial cells, AR+, Sox17+ breast epithelial cells, AR+, Cox2+ breast epithelial cells, and/or AR+, Ki67+ breast epithelial cells are determined, as described above.

In one embodiment of the above method, the frequency of the p27+ breast epithelial cells, Ki67+ breast epithelial cells, Sox17+ breast epithelial cells, Cox2+ breast epithelial cells, ER+, p27+ breast epithelial cells, ER+, Sox17+ breast epithelial cells, ER+, Cox2+ breast epithelial cells, ER+, Ki67+ breast epithelial cells, AR+, p27+ breast epithelial cells, AR+, Sox17+ breast epithelial cells, AR+, Cox2+ breast epithelial cells, and/or AR+, Ki67+ breast epithelial cells is increased relative to the first control frequency by at least 2-fold, at least 3-fold, at least 4-fold, at least 5-fold, at least 10-fold, or more. Also preferably, in the above method, the frequency of the p27+, Ki67+, Sox17 and/or Cox2+ breast epithelial cells is decreased relative to the second control frequency by at least 2-fold, at least 3-fold, at least 4-fold, at least 5-fold, at least 10-fold, or more.

In another embodiment, step (b) of the method described above comprises predicting that the subject has a relatively elevated risk of developing breast cancer if the frequency of p27+ breast epithelial cells is 15% or greater (e.g., 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40% or greater) of the breast epithelial cells in the sample; and step (c) comprises predicting that the subject has a relatively reduced risk of developing breast cancer if the frequency of p27+ breast epithelial cells is less than 15% (e.g., 14%, 13%, 12%, 11%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1% or less) of the breast epithelial cells in the sample.

In another embodiment, step (b) of the method described above comprises predicting that the subject has a relatively elevated risk of developing breast cancer if the frequency of Ki67+ breast epithelial cells is 2% or greater or 3% of greater of the breast epithelial cells in the sample, and step (c) comprises predicting that the subject has a relatively reduced risk of developing breast cancer if the frequency of Ki67+ breast epithelial cells is less than 2% (e.g., 1.9%, 1.8%, 1.7%, 1.6%, 1.5%, 1.0%, 0.5%, or 0%) of the breast epithelial cells in the sample.

In another embodiment, step (b) of the method described above comprises predicting that the subject has a relatively elevated risk of developing breast cancer if the frequency of p27+ breast epithelial cells is 15% or greater (e.g., 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40% or greater) of the breast epithelial cells in the sample; and step (c) comprises predicting that the subject has a relatively reduced risk of developing breast cancer if the frequency of p27+ breast epithelial cells is less than 15% (e.g., 14%, 13%, 12%, 11%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1% or less) of the breast epithelial cells in the sample.

In another embodiment, step (b) of the method described above comprises predicting that the subject has a relatively elevated risk of developing breast cancer if the frequency of p27+, AR+ breast epithelial cells is 10% or greater (e.g., 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40% or greater) of the breast epithelial cells in the sample; and step (c) comprises predicting that the subject has a relatively reduced risk of developing breast cancer if the frequency of p27+ breast epithelial cells is less than 10% (e.g., 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1% or less) of the breast epithelial cells in the sample.

In yet other embodiments, a method of predicting a subject's risk of developing an breast cancer is provided, wherein the method comprises: (a) determining the expression level in a breast tissue sample from a subject of at least one marker, e.g., p27, Sox17 and Cox2; and (b) predicting that the subject has a relatively elevated risk of developing breast cancer if the expression level of the at least one marker is increased compared to a first control level of the at least one marker; or (c) predicting that the subject has a relatively reduced risk of developing breast cancer if the expression level of the at least one marker is decreased compared to a second control level of the at least one marker. Methods for determining the expression level of markers p27, Sox17 and Cox2 (e.g., QPCR, FACS, immunohistochemistry, Western blot, ELISA) are described above.

In step (b) in the above method, preferably, the expression level of p27, Sox17 and/or Cox2 (e.g., mRNA and/or polypeptide) is increased by at least 2-fold, at least 3-fold, at least 4-fold, at least 5-fold, at least 6-fold, at least 7-fold, at least 8-fold, at least 9-fold, at least 10-fold, at least 20-fold or greater, compared to the first control level (i.e., a control level from a subject known to be at low risk of developing breast cancer). In step (c) in the above method, preferably, the expression level of p27, Sox17 and/or Cox2 (e.g., mRNA and/or polypeptide) is decreased by at least 2-fold, at least 3-fold, at least 4-fold, at least 5-fold, at least 6-fold, at least 7-fold, at least 8-fold, at least 9-fold, at least 10-fold, at least 20-fold or more, compared to the second control level (i.e., a control level from a subject known to be at high risk of developing breast cancer).

In still other embodiments, methods of predicting the risk of developing breast cancer are provided, which comprise determining a parity/nulliparity-associated gene expression signature in a sample comprising breast epithelial cells. Also provided are methods of predicting breast cancer disease outcome by testing for a parity/nulliparity-associated gene expression signature in breast cancer cells.

As described above and in Example 10, the genes that were shown to be upregulated or downregulated in FIG. 28 make up a parity/nulliparity-related gene signature. Further, the genes for which the expression profile is shown in FIG. 28 are described in detail in Table 18, below. Of course, the skilled artisan will appreciate that a parity/nulliparity-related gene signature can, but does not necessarily comprise all of the genes shown in Table 18. Such gene signature comprises 2 or more, 3 or more, 4 or more, 5 or more, 10 or more, 15 or more, 20 or more, 30 or more, 40 or more, 50 or more, or 100 or more of the genes shown in Table 18.

Further, for each of the genes shown in Table 18, the disease outcome based on the expression of a particular gene in the expression is shown (i.e., a prognosis of “good” or “bad”). Thus, the skilled artisan can select one or more genes from the list of genes in Table 18 that are correlated with a “good” prognosis and/or one or more genes associated with a “bad” prognosis, and assemble the selected genes in a custom gene signature. A subject's gene expression profile for the genes in the custom signature can be determined, and for example, if the subject expresses more of the genes associated with a “bad” prognosis than the genes associated with a “good” prognosis, then the patient's disease outcome is predicted to be “bad” or “poor”, whereas as subject expressing more of the “good” prognosis gens is predicted to have a “good” prognosis (i.e., more likely to survive the disease).

The above described methods of predicting a subject's risk of developing cancer and for determining a dise outcome (e.g., prognosis), can be used, e.g., by the subject's physician to determine the best course of treatment or prophylaxis to administer to the subject in need thereof, as well as other courses of action. For example, such methods can further comprise administering to a subject identified as having an increased risk of developing breast cancer, or a subject diagnosed with breast cancer and determined according to the above methods to have a bad prognosis, a therapy or therapeutic agent for treating, reducing the risk of developing, or preventing breast cancer (e.g., ER+ or ER− breast cancer). In other embodiments, the methods can comprise performing additional diagnostic assays to confirm the diagnosis (e.g., imaging, biopsy, etc.), recording the diagnosis in a database or medical history (e.g., medical records) of the subject, performing diagnostic tests on a family member of the subject, selecting the subject for increased monitoring or periodically monitoring the health of the subject (e.g., for development of signs or symptoms of breast cancer, e.g., tumor development or tumor size changes (e.g., increased or decreased size), such as e.g., clinical breast exam, mammography, MRI, or other suitable imaging or other diagnostic method(s) known in the art.

VII. ADMINISTRATION

Compositions and formulations comprising an inhibitor or agonist of the invention (e.g., an inhibitor or agonist of a gene or polypeptide mediating a function in a pathway that is upregulated or downregulated in breast epithelial cells of nulliparous women), can be administered topically, parenterally, orally, by inhalation, as a suppository, or by other methods known in the art. The term “parenteral” includes injection (for example, intravenous, intraperitoneal, epidural, intrathecal, intramuscular, intraluminal, intratracheal or subcutaneous). Exemplary routes of administration include, e.g., intravenous, intraductal, and intratumoral.

While it is possible to use an inhibitor or agonist of the invention for therapy as is, it may be preferable to administer an inhibitor or agonist as a pharmaceutical formulation, e.g., in admixture with a suitable pharmaceutical excipient, diluent, or carrier selected with regard to the intended route of administration and standard pharmaceutical practice. Pharmaceutical formulations comprise at least one active compound, or a pharmaceutically acceptable derivative thereof, in association with a pharmaceutically acceptable excipient, diluent, and/or carrier. The excipient, diluent and/or carrier must be “acceptable,” as defined above.

Administration of a composition or formulation of the invention can be once a day, twice a day, or more often. Frequency may be decreased during a treatment maintenance phase of the disease or disorder, e.g., once every second or third day instead of every day or twice a day. The dose and the administration frequency will depend on the clinical signs, which confirm maintenance of the remission phase, with the reduction or absence of at least one or more preferably more than one clinical signs of the acute phase known to the person skilled in the art. More generally, dose and frequency will depend in part on recession of pathological signs and clinical and subclinical symptoms of a disease condition or disorder contemplated for treatment with the present compounds.

It will be appreciated that the amount of an inhibitor required for use in treatment will vary with the route of administration, the nature of the condition for which treatment is required, and the age, body weight and condition of the patient, and will be ultimately at the discretion of the attendant physician or veterinarian. Compositions will typically contain an effective amount of the active agent(s), alone or in combination. Preliminary doses can be determined according to animal tests, and the scaling of dosages for human administration can be performed according to art-accepted practices.

Length of treatment, i.e., number of days, will be readily determined by a physician treating the subject; however the number of days of treatment may range from 1 day to about 20 days. As provided by the present methods, and discussed below, the efficacy of treatment can be monitored during the course of treatment to determine whether the treatment has been successful, or whether additional (or modified) treatment is necessary.

VIII. METHODS OF TREATING AND PREVENTING BREAST CANCER

Provided herein are methods for treating and preventing estrogen-receptor-positive (ER+) breast cancer in a subject. Typically, a subject that can be administered an inhibitor or agonist, or composition, e.g., pharmaceutical composition, comprising one or more inhibitors or agonists described above is a premenopausal or postmenopausal woman. In some embodiments, the subject has a BRCA-1 or BRCA-2 germline mutation.

In certain embodiments, methods of treating breast cancer (e.g., ER+ or ER− breast cancer) in a subject are provided that comprise administering to the subject a composition comprising an inhibitor of a pathway that has increased activity in breast epithelial cells (e.g., CD44+, CD24− breast epithelial cells) of nulliparous women compared to the activity in breast epithelial cells of parous women (i.e. a pathway active in nulliparous breast epithelial cells). In other embodiments an agonist of a pathway that has decreased activity in breast epithelial cells (e.g., CD44+, CD24− breast epithelial cells) of nulliparous women compared to the activity in breast epithelial cells of parous women (i.e. a pathway active in parous breast epithelial cells) can be administered. Such inhibitors and agonists and the target pathways and genes in those pathways are described in detail above.

In other embodiments, methods of preventing breast cancer (e.g., ER+ or ER− breast cancer) in a subject are provided that comprise administering to a subject at risk of developing breast cancer an inhibitor of a pathway active in nulliparous breast epithelial cells (e.g., CD44+, CD24− breast epithelial cells). For example, the pathway can include a mediator molecule such as cAMP, EGFR, Cox2, Hh, TGFBR, and IGFR, as described above. In another embodiment, the method of preventing breast cancer in a subject comprises administering to the subject an agonist of a pathway active in parous breast epithelial cells (e.g., CD44+, CD24-breast epithelial cells) (e.g., an agonist of Hakai/CBLL1, CASP8, SCRIB, LLGL2, PI3K/AKT signaling, and apoptosis).

In certain embodiments, an inhibitor or agonist or any combination of 2 or more, 3 or more, 4 or more, or 5 or more inhibitors and/or agonists of the above-described target genes and/or polypeptides can be administered in a combination therapy to a subject for the treatment or prevention of breast cancer (e.g., ER+ or ER− breast cancer).

The skilled artisan will appreciate that other combinations of inhibitors and/or agonists are possible, so long as the combination results in the treatment or prevention of breast cancer.

The skilled artisan will also appreciate that the methods of treating breast cancer described herein (e.g., administration of one or more of the inhibitors and agonists described above) may also be administered in a combination therapy with other treatments, e.g. other cancer therapies. Non-limiting examples of such cancer therapies include, e.g., chemotherapy, radiation therapy, biological therapy (e.g., antibodies, biological modifiers (cytokines, growth factors, lymphokines, chemokines, etc.), immune cell therapies (LAK cells, tumor specific CTL, etc.), anti-angiogenic therapy, surgery, and combinations thereof.

Chemotherapeutic agents, include for example: taxanes such as taxol, taxotere or their analogues; alkylating agents such as cyclophosphamide, isosfamide, melphalan, hexamethylmelamine, thiotepa or dacarbazine; antimetabolites such as pyrimidine analogues, for instance 5-fluorouracil, cytarabine, capecitabine, and gemcitabine or its analogues such as 2-fluorodeoxycytidine; folic acid analogues such as methotrexate, idatrexate or trimetrexate; spindle poisons including vinca alkaloids such as vinblastine, vincristine, vinorelbine and vindesine, or their synthetic analogues such as navelbine, or estramustine and a taxoid; platinum compounds such as cisplatin; epipodophyllotoxins such as etoposide or teniposide; antibiotics such as daunorubicin, doxorubicin, bleomycin or mitomycin, enzymes such as L-asparaginase, topoisomerase inhibitors such as topotecan or pyridobenzoindole derivatives; and various agents such as procarbazine, mitoxantrone, and biological response modifiers or growth factor inhibitors such as interferons or interleukins. Other chemotherapeutic agents include, though are not limited to, a p38/JAK kinase inhibitor, e.g., SB203580; a phospatidyl inositol-3 kinase (PI3K) inhibitor, e.g., LY294002; a MAPK inhibitor, e.g. PD98059; a JAK inhibitor, e.g., AG490; preferred chemotherapeutics such as UCN-01, NCS, mitomycin C (MMC), NCS, and anisomycin; taxoids in addition to those describe above (e.g., as disclosed in U.S. Pat. Nos. 4,857,653; 4,814,470; 4,924,011, 5,290,957; 5,292,921; 5,438,072; 5,587,493; European Patent No. 0 253 738; and PCT Publication Nos. WO 91/17976, WO 93/00928, WO 93/00929, and WO 96/01815. In other embodiments, a cancer therapy can include but is not limited to administration of cytokines and growth factors such as interferon (IFN)-gamma, tumor necrosis factor (TNF)-alpha, TNF-beta, and/or similar cytokines, or an antagonist of a tumor growth factor (e.g., TGF-β and IL-10). Antiangiogenic agents, include, e.g., endostatin, angiostatin, TNP-470, Caplostatin (Stachi-Fainaro et al., Cancer Cell 7(3), 251 (2005)). Drugs that interfere with intracellular protein synthesis can also be used in the methods of the present invention; such drugs are known to those skilled in the art and include puromycin, cycloheximide, and ribonuclease.

For radiation therapy, common sources of radiation used for cancer treatment include, but are not limited to, high-energy photons that come from radioactive sources such as cobalt, cesium, iodine, palladium, or a linear accelerator, proton beams; neutron beams (often used for cancers of the head, neck, and prostate and for inoperable tumors), x or gamma radiation, electron beams, etc.

It is well known that radioisotopes, drugs, and toxins can be conjugated to antibodies or antibody fragments which specifically bind to markers which are produced by or associated with cancer cells, and that such antibody conjugates can be used to target the radioisotopes, drugs or toxins to tumor sites to enhance their therapeutic efficacy and minimize side effects. Examples of these agents and methods are reviewed in Wawrzynczak and Thorpe (in Introduction to the Cellular and Molecular Biology of Cancer, L. M. Franks and N. M. Teich, eds, Chapter 18, pp. 378-410, Oxford University Press. Oxford, 1986), in Immunoconjugates: Antibody Conjugates in Radioimaging and Therapy of Cancer (C. W. Vogel, ed., 3-300, Oxford University Press, N.Y., 1987), in Dillman, R. 0. (CRC Critical Reviews in Oncology/Hematology 1:357, CRC Press, Inc., 1984), in Pastan et al. (Cell 47:641, 1986) in Vitetta et al. (Science 238:1098-1104, 1987) and in Brady et al. (Int. J. Rad. Oncol. Biol. Phys. 13:1535-1544, 1987). Other examples of the use of immunoconjugates for cancer and other forms of therapy have been disclosed, inter alia, in U.S. Pat. Nos. 4,331,647, 4,348,376, 4,361,544, 4,468,457, 4,444,744, 4,460,459, 4,460,561 4,624,846, 4,818,709, 4,046,722, 4,671,958, 4,046,784, 5,332,567, 5,443,953, 5,541,297, 5,601,825, 5,637,288, 5,677,427, 5,686,578, 5,698,178, 5,789,554, 5,922,302, 6,187,287, and 6,319,500.

IX. METHODS FOR DETERMINING EFFICACY OF A BREAST CANCER THERAPY

In certain embodiments, methods for determining the efficacy of a breast cancer therapy (including prophylactic therapy) are provided. The therapy can be a therapy described herein or any other conventional breast cancer therapy. In one embodiment, the efficacy of a cancer therapy is determined by comparing a subject's parity/nulliparity-related gene expression profile before treatment for the breast cancer to the subject's parity/nulliparity-related gene expression profile during or after the treatment. Typically, a subject that is in need of breast cancer treatment (including prophylactic therapy, e.g., for a subject determined to have an elevated risk of developing breast cancer) will have a parity/nulliparity-related gene expression profile that most closely resembles (i.e., is the same or similar to) the gene signature for nulliparous women. After a successful therapy, it is expected that the subject's gene expression profile will more closely resemble the parity/nulliparity-related gene expression profile of parous women, as described herein (e.g., FIG. 28 and Table 18). A gene signature not resembling the gene expression profile of parous women is an indication that the treatment was not successful, and further treatment or a different treatment is needed.

In other embodiments, a method for determining efficacy of an breast cancer therapy (including prophylactic therapy) comprises measuring the level of a specific gene and/or polypeptide before and after (or during the therapy). For example, as described above, in certain embodiments a method for treating or preventing breast cancer comprises administering an inhibitor or agonist of a specific gene or polypeptide. The level or activity of the target gene or polypeptide can be measured before or at the beginning of treatment, and then again during of after treatment; typically, when an inhibitor is administered as a cancer therapy, the inhibition and therapy is deemed effective if the level or activity of the target gene or polypeptide is decreased by at least 2-fold, at least 3-fold, at least 4-fold, at least 5-fold, at least 10-fold, or more, relative to the level of the target gene or polypeptide at the beginning of or before commencement of the cancer therapy. Typically, when an agonist is administered as a cancer therapy, the inhibition and therapy is deemed effective if the level or activity of the target gene or polypeptide is increased by at least 2-fold, at least 3-fold, at least 4-fold, at least 5-fold, at least 10-fold, or more, relative to the level of the target gene or polypeptide at the beginning of or before commencement of the cancer therapy.

The above described methods can further comprise administering to the subject (e.g., a subject in which the efficacy of the breast cancer therapy was determined to be poor or not optimal) an additional therapy or therapeutic agent for treating, reducing the risk of developing, or preventing breast cancer (e.g., ER+ or ER− breast cancer). In other embodiments, the methods can comprise recording the results in a database or medical history (e.g., medical records) of the subject, selecting the subject for increased monitoring or periodically monitoring the health of the subject (e.g., for development or changes in the signs or symptoms of the breast cancer, e.g., tumor development and/or changes in tumor size (e.g., increased or decreased size), such as e.g., clinical breast exam, mammography, MRI, or other suitable imaging or other diagnostic method(s) known in the art.

Methods for determining the level of a target gene or polypeptide are well known in the art, as described above.

As above, such methods can be conducted in parallel, or before or after, conventional methods for determining success of a treatment, such as, e.g. measuring tumor size or other symptoms of breast cancer known in the art.

X. KITS

In certain embodiments, kits are provided for predicting a subject's risk of developing breast cancer. In other embodiments, kits are provided for predicting a subject's breast cancer disease outcome (i.e., prognosis, e.g., likeliness to survive the disease). In other embodiments, kits are provided for treating breast cancer. In still other embodiments, kits are provided for determining the efficacy of a cancer therapy.

The above kits can comprise means (e.g., reagents, dishes, solid substrates (e.g., microarray slides, ELISA plates, multiplex beads), solutions, media, buffers, etc.) for determining the level of expression or activity of one or more of the genes and/or pathways described herein. Such kits can further comprise instructions for use, e.g., guidelines for determining the efficacy of a cancer therapy, or for predicting a subject's risk of developing breast cancer, based on the level of expression or activity of the one or more genes detected using the kit.

Other kits comprise means for determining (e.g., reagents, dishes, solid substrates (e.g., microarray slides, ELISA plates, multiplex beads), solutions, media, buffers, etc.) the frequency of breast epithelial cell types (e.g., the frequency of CD44+, CD24− breast epithelial cells, CD24+ breast epithelial cells, CD10+ breast epithelial cells, p27+ breast epithelial cells, Ki67+ breast epithelial cells, Sox17+ breast epithelial cells and/or Cox2+ breast epithelial cells, and/or ER+, p27+ breast epithelial cells, ER+, Sox17+ breast epithelial cells, ER+, Cox2+ breast epithelial cells, ER+, Ki67+ breast epithelial cells, AR+, p27+ breast epithelial cells, AR+, Sox17+ breast epithelial cells, AR+, Cox2+ breast epithelial cells, and/or AR+, Ki67+ breast epithelial cells). Such kits can comprise means for detecting expression (e.g., mRNA and/or protein) levels of one or more of the markers (e.g., CD44, CD24, CD10, p27, Ki67, Sox17, and/or Cox2) of the cell types described above. Such kits can also comprise instructions for determining a subject's risk of developing breast cancer based on the frequencies of those cell types determined. The frequencies that indicate an elevated or reduced risk of developing breast cancer are disclosed above and in the present Examples.

Other kits can comprise means for determining a parity/nulliparity gene expression profile. For example, such kits can comprise a microarray slide or slides comprising probes for two or more genes making up the parity/nulliparity gene expression profile, or means for performing PCR (e.g., QPCR), such as forward and reverse primers, reverse transcriptase, plates, and/or other PCR reagents. Such kits can further comprise instructions for determining a subject's disease outcome based on the subject's parity/nulliparity gene expression profile, as described above and in the present Examples, and may also provide a standard or reference gene expression profile for comparison.

Other kits comprise one or more inhibitors or agonists of pathways active in nulliparous or parous breast epithelial cells (e.g. CD44+, CD24− breast epithelial cells), as described herein, for the treatment or prevention of breast cancer (e.g., ER+ or ER− breast cancer), and, optionally instructions for use (e.g. administration and/or dosage).

In other embodiments, a kit comprises an array containing a substrate having at least 10, 25, 50, 100, 200, 500, or 1,000 addresses, wherein each address has disposed thereon a capture probe that includes: (a) a nucleic acid sequence consisting of a tag nucleotide sequence for the detection of a gene identified in Tables 4, 5, 6, 7 and/or 18 (e.g., HSD17B11, HSD17B12, HSD17B14, HSP90AB1 (GenBank Accession No. AAH09206), PSA (KLK3), NCOR1, NCOR2, NCOA4, NCOA7, SFRP2, SFRP4, VEGFA, NOTCH1, FN1, ITGA4, ITGB1, TSPAN6, RhoA, RAC1, CDC42, PHB4, BCL2L11, TNFRSF4, BMPR2, CASP8, PP2A, PIK3CG, ILK, PDPK1, Hakai/CBLL1, SCRIB, and LLGL2, MAP2K4 (GenBank Accession No. NM_(—)003010.2), PTP4A2 (GenBank Accession No. NM_(—)080391.3), EPHB4 (GenBank Accession No. NM_(—)004444), SPARC (GenBank Accession No. NM_(—)003118.3), RAB32 (GenBank Accession No. NM_(—)006834.3), FIGF (GenBank Accession No. NM_(—)004469.4), SNX3 (GenBank Accession Nos. NM_(—)003795.4, NM_(—)152827.2), GADD45A (GenBank Accession Nos. NM_(—)001924.3, NM_(—)001199741.1, NM_(—)001199742.1), ANXA3 (GenBank Accession Nos. NM_(—)005139.2), and HSPA2 (GenBank Accession No. NM_(—)021979.3)); and (b) the complement of the nucleic acid sequence.

Another kit provided herein contains at least 10 antibodies each of which is specific for a different protein encoded by a gene identified in Tables 4, 5, 6, 7 and/or 18. The antibodies can be, for example, but not limited to, specific for a protein such as HSD17B11, HSD17B12, HSD17B14, HSP90AB1 (GenBank Accession No. AAH09206), PSA (KLK3), NCOR1, NCOR2, NCOA4, NCOA7, SFRP2, SFRP4, VEGFA, NOTCH1, FN1, ITGA4, ITGB1, TSPAN6, RhoA, RAC1, CDC42, PHB4, BCL2L11, TNFRSF4, BMPR2, CASP8, PP2A, PIK3CG, ILK, PDPK1, Hakai/CBLL1, SCRIB, and LLGL2, MAP2K4 (GenBank Accession No. NM_(—)003010.2), PTP4A2 (GenBank Accession No. NM_(—)080391.3), EPHB4 (GenBank Accession No. NM_(—)004444), SPARC (GenBank Accession No. NM_(—)003118.3), RAB32 (GenBank Accession No. NM_(—)006834.3), FIGF (GenBank Accession No. NM_(—)004469.4), SNX3 (GenBank Accession Nos. NM_(—)003795.4, NM_(—)152827.2), GADD45A (GenBank Accession Nos. NM_(—)001924.3, NM_(—)001199741.1, NM_(—)001199742.1), ANXA3 (GenBank Accession Nos. NM_(—)005139.2), and HSPA2 (GenBank Accession No. NM_(—)021979.3). The kit can contain at least 5 antibodies, at least 10 antibodies, at least 15 antibodies, at least 25 antibodies; at least 50 antibodies; at least 100 antibodies; at least 200 antibodies; or at least 500 antibodies.

The kits, regardless of type, will generally comprise one or more containers into which the biological agents (e.g. inhibitors) are placed and, preferably, suitably aliquotted. The components of the kits may be packaged either in aqueous media or in lyophilized form.

In accordance with the present invention, there may be employed conventional molecular biology, microbiology, recombinant DNA, immunology, cell biology and other related techniques within the skill of the art. See, e.g., Sambrook et al., (2001) Molecular Cloning: A Laboratory Manual. 3rd ed. Cold Spring Harbor Laboratory Press: Cold Spring Harbor, N.Y.; Sambrook et al., (1989) Molecular Cloning: A Laboratory Manual. 2nd ed. Cold Spring Harbor Laboratory Press: Cold Spring Harbor, N.Y.; Ausubel et al., eds. (2005) Current Protocols in Molecular Biology. John Wiley and Sons, Inc.: Hoboken, N.J.; Bonifacino et al., eds. (2005) Current Protocols in Cell Biology. John Wiley and Sons, Inc.: Hoboken, N.J.; Coligan et al., eds. (2005) Current Protocols in Immunology, John Wiley and Sons, Inc.: Hoboken, N.J.; Coico et al., eds. (2005) Current Protocols in Microbiology, John Wiley and Sons, Inc.: Hoboken, N.J.; Coligan et al., eds. (2005) Current Protocols in Protein Science, John Wiley and Sons, Inc.: Hoboken, N.J.; Enna et al., eds. (2005) Current Protocols in Pharmacology John Wiley and Sons, Inc.: Hoboken, N.J.; Hames et al., eds. (1999) Protein Expression: A Practical Approach. Oxford University Press: Oxford; Freshney (2000) Culture of Animal Cells: A Manual of Basic Technique. 4th ed. Wiley-Liss; among others. The Current Protocols listed above are updated several times every year.

The following examples are meant to illustrate, not limit, the invention.

EXAMPLES Example 1 Materials and Methods

The following are the materials and methods used in the Examples set forth below.

FACS (Fluorescence Activated Cell Sorting)

A single-cell suspension of human mammary epithelial cells was obtained from organoids after trypsinization (5 mins, 37° C.) and filtration through 40 μm cell strainers. Leukocytes, fibroblasts, and endothelial cells were removed by immuno-magnetic bead purification using cell-type-specific surface markers essentially as previously described [Bloushtain-Qimron, et al. (2008). Proc Natl Acad Sci USA 105, 14076-14081; Shipitsin, M., et al. (2007). Cancer Cell 11, 259-273]. Cells were re-suspended in ice cold PBE (0.5% BSA and 2 mM EDTA in PBS) at 2×10⁶ cells/ml. 2×10⁵ cells from each sample were used for multicolor FACS analysis. Cells were stained with propidium iodine (PI, Sigma), FITC conjugated anti-human EpCAM (Dako, clone Ber-Ep4), PE-conjugated anti-human CD49f (BD, clone GoH3), PE/Cy7-conjugated anti-human CD10 (Biolegend, Clone HI10a), APC-conjugated anti-human CD24 (Biolegend, clone ML5), and purified anti-human CD44 (BD, Clone 515). CD44 antibody was pre-labeled with Zenon Alexa 405 mouse IgG1 kit (Invitrogen). Only PI-negative (viable cells) were used to calculate the relative fraction of each cell population.

Multicolor Immunofluorescence and Immunohistochemical Analyses

Multicolor immunofluorescence for CD44 (Neomarkers, clone 156-3C11, mouse monoclonal IgG2), CD24 (SWAII clone, generously provided by Dr. Peter Altevogt (German Cancer Research Center, Heidelberg, Germany), mouse monoclonal IgG2), p27 (BD Biosciences, clone 57/Kip1/p27, mouse monoclonal IgG1), Sox17 (R&D Systems, clone 245013, mouse monoclonal IgG3), COX2 (Cayman Chemical, clone CX229, mouse monoclonal IgG1), Ki67 (DAKO, clone MIB-1, mouse monoclonal IgG1), Ki67 (Abcam, #16667, rabbit monoclonal) and bromodeoxyuridine (BrdU, Roche, clone BMC9318, mouse monoclonal IgG1), CD10 (DAKO M7308), p63 clone 4A4 (Santa Cruz SC-8431), SMA clone 1A4 (DAKO M0851), Axin2 clone 354214 (R&D systems MAB6078), Phosphor-EGF Receptor (Tyr1173) clone 53A5 (Cell Signaling #4407), Phospho-Smad2 (Ser 465/467) (Cell Signaling #3101), Gata3 (Santa Cruz SC-268), estrogen receptor (clone SP1, Thermo Scientific RM-9101), androgen receptor (clone D6F11, Cell Signalling #5153), and bromodeoxyuridine (BrdU, Roche, clone BMC9318), was performed using whole sections of formalin fixed paraffin embedded (FFPE) normal human breast tissue.

The tissues were deparaffinized in xylene and hydrated in a series of 100%, 70%, 50% and 0% ethanol solutions. After heat-induced antigen retrieval in citrate buffer (pH 6), the samples were blocked with goat serum and sequentially stained with the different primary and secondary antibodies. The sequential staining was optimized to avoid cross-reaction between antibodies and was performed as follows: monoclonal (IgG2a) antibody anti-CD44 (1:100 dilution) for one hour at room temperature; goat anti-mouse IgG2a A1exa555-conjugated (Invitrogen, 1:100 dilution) for 30 minutes at room temperature; monoclonal antibody anti-p27 (1:100 dilution) or monoclonal antibody anti-Sox17 (1:50 dilution) or anti-COX2 (1:50 dilution), and monoclonal antibody anti-CD24 (1:25 dilution) biotin labeled (Zenon® Biotin-XX Rabbit IgG Labeling Kit, Invitrogen), p63 (1:100 dilution), SMA (1:80 dilution), CD10 (1:100 dilution), Gata3 (1:50 dilution) for one hour at room temperature; goat anti-mouse IgG1 Alexa 488-conjugated (Invitrogen, 1:100 dilution, for detection of p27 or COX2), goat anti-mouse Alexa 488/555/647 (Invitrogen 1:100 dilution, for detection of p63, SMA, CD10 and Gata3) or goat anti-mouse IgG3 Alexa 488-conjugated (Invitrogen, 1:100 dilution, for detection of Sox17) and streptavidin Alexa-647 conjugated for 30 minutes at room temperature.

The multicolor immunofluorescence for p27 and Ki67 was performed by incubating the samples with monoclonal antibody anti-p27 (1:100 dilution) and polyclonal antibody anti-Ki67 (1:50 dilution) for one hour at room temperature followed by goat anti-mouse IgG1 Alexa 555-conjugated (Invitrogen, 1:100 dilution, for detection of p27) and goat anti-rabbit Alexa 488-conjugated (Invitrogen, 1:100 dilution, for detection of Ki67) for 30 minutes at room temperature. Multicolor immunofluorescence for pSMAD2 (1:50 dilution), pEGFR (1:50 dilution) and Axin2 (1:20 dilution) were performed by incubation for 2 h at room temperature or overnight at 4° C. followed by secondary antibody Rabbit Alexa 488 conjugated (Invitrogen, 1:100 dilution for pSMAD2 and pEGFR) or mouse IgG1 Alexa-488 conjugated (Invitrogen 1:100 dilution) for Axin2 for 30 minutes at room temperature.

The samples were washed twice with PBS-Tween 0.05% between incubations and protected for long-term storage with VECTASHIELD HardSet Mounting Medium with DAPI (Vector laboratories, cat #H-1500). Before image analysis, the samples were stored at −20° C. for at least 48 hours. Different immunofluorescence images from multiple areas of each sample were acquired with a Nikon Ti microscope attached to a Yokogawa spinning-disk confocal unit, 60× plan apo objective, and OrcaER camera controlled by Andor iQ software. For the immunohistochemical detection of Sox17 and COX2 the samples were stained with antibodies against Sox17 and COX2 as above, and then incubated with anti-mouse IgG biotinylated antibody (1:100 dilution) for 30 minutes at room temperature followed by the ABC peroxidase System (Vectastain®, ABC System Vector Laboratories). DAB (3,3′-diaminodbenzidine) was used as colorimetric substrate and the signal was enhanced by the addition of 0.04% of nickel chloride. The slides were finally counterstained with Methyl green.

Scoring for the expression of each marker was done as follows: p27 fluorescence intensity was scored in the nuclei of 20 randomly selected cells using the ImageJ 1.43r software; Sox17 and COX2 expression was inferred by the combination of two variables: 1) the percentage cells expressing each marker, and 2) the intensity of each marker transformed into a categorical variable based on 0 no expression, 1 weak expression, 2 moderate expression and 3 high expression; the percentage of p27+, Ki67+ and BrdU+ cells was estimated by counting an average of 1000 cells/sample in the case of the mammary epithelium for premenopausal, postmenopausal and high-low density cases, and an average of 2,000 cells in the case of the tissue slices cultures. % of pSMAD2+ cells was estimated by counting an average of 600 cells/sample. For pEGFR and Axin2 fluorescence intensity measurement, mean fluorescence intensity was measured using Image J 1.43r software by counting an average of 600 cells/sample corrected by area and subtracting the average of background fluorescence intensity. RGB profile was also generated using Image J 1.43 software. For multicolor immunofluorescence of p27 and ER, p27 (1:100 dilution) and ER (1:500 dilution) antibodies were incubated overnight at 40 C followed by incubation at RT for 1 h with subsequent staining by goat anti-mouse IgG1 Alexa 555-conjugated (Invitrogen, 1:100 dilution, for detection of p27) while detection of ER antibody was performed by Biotinylated anti Rabbit 20 antibody (1:100 dilution) using Perkin Elmer TSATM INDIRECT tyramide amplification kit (NEL700001KT) and streptavidin conjugated Alexa 647 from Invitrogen (1:80 dilution). For p27 and AR staining, p27 (1:100 dilution and AR (1:30 dilution) antibodies were incubated overnight at 40 C followed by incubation at RT for 1 h with subsequent staining by goat anti-mouse IgG1 Alexa 555-conjugated (Invitrogen, 1:100 dilution, for detection of p27) and anti-rabbit IgG Alexa 488-conjugated (Invitrogen, 1:80 dilution). Percentage of p27+, AR+, ER+ cells was estimated by counting 500-1000 cells/sample. Nuclear staining with DAPI and multiple fluorescence images from each section were acquired with 40× plan apo objective, following procedure described above.

Culture of Tissue Slices

Normal human breast tissues were collected from reduction mammoplasties, transported in ice-cold DMEM-F12 medium, and processed within 24 hrs. For organ cultures, thin (˜1 mm thick) slices of tissue were cut from epithelium-enriched areas and cultured for 8 days in 6-well plates using co-culture inserts to optimize the tissue/medium contact surface and changing medium (2 ml/well) every 24 hrs. The M87A medium previously optimized for human primary mammary epithelial cultures was used [see, Bloushtain-Qimron, et al. (2008) supra; Garbe, J. C., et al. (2009). Cancer Res 69, 7557-7568]. Inhibitors used included cyclopamine (Selleck Chemicals, cat#S1146)—inhibitor of Smo receptor of Hh ligands, LY2109761 (Eli Lilly)—inhibitor of TGFBR kinases, celecoxib (LKT laboratories, cat#C1644)—inhibitor of Cox2, 2-5dideoxyadenosine (Enzo Life Sciences, cat#BML-CN110-005)—adenylate cyclase inhibitor, tyrphostin AG1478 (Cayman Chemicals, cat#10010244)—EGFR inhibitor, XAV939 (Tocris Bioscience, cat#3748)—Tankyrase (TNKS) inhibitor- antagonizes Wnt signaling via stimulation of β-catenin degradation and stabilization of axin, picropodophylotoxin (Tocris Bioscience, cat#2956)—IGFR inhibitor Stock solutions (1,000×) were prepared in DMSO. Final drug concentrations were as follows: cyclopamine—10 μM, LY2109761-500 nM, celecoxib—100 μM, 2-5dideoxyadenosine—100 μM, AG1478—10 μM, XAV939—1 μM and Picropodophylotoxin—0.5 μM. Following 8 days of culture, labeled tissue slices were pulse with bromo-deoxy-uridine (30 μM final concentration) for 5 hrs before fixing the tissue in buffered formalin at room temperature for 24 hrs followed by embedding in paraffin. Experiments were performed in triplicates using tissue from different regions of the same breast, uncultured tissue and tissue cultured without any drugs as controls. To experimentally reproduce hormone levels in follicular and luteal phase of the menstrual cycle and in mid-pregnancy, the following was used: 0.5 nM of estradiol for 8 days to mimic follicular phase; 1.2 nM of estradiol for 2 days (representing ovulation) followed by 0.7 nM of estradiol and 50 nM of progesterone for 6 days to mimic luteal phase; and a combination of 250 nM estradiol, 600 nM progesterone, 600 ng/mL prolactin, and 10 IU/mL HCG for 8 days to mimic pregnancy in the normal breast.

PCA Analysis and Plot

Unsupervised principle component analysis (PCA) was applied using R package ‘pcurve’ to gene expression profiles of different cell types from parous and nulliparous tissues. The mean of each sample was centered to zero before PCA analysis. Genes were the feature variables and samples were projected to the principle components. OpenGL was used to plot PCA results by projecting each sample to the first three principal components. Using the projected value on the largest 3 principal component as the Euclidean coordinates for each individual, paired Euclidean distance between nulliparous and parous individuals for each cell type was calculated. The distance is a global measurement of the difference between individuals. It indicated, for example, that the gene expression of CD44⁺ cells changed the most, as it has the most significant distance between nulliparous and parous samples.

Rat Gene Expression Data Analysis and Comparison with Human

Previously published gene expression data from virgin and parous rats was reanalyzed using four (WistarFurth, Copenhagan, Fischer344, and Lewis) inbred strains of rats [Blakely, C. M., et al. (2006). Cancer Res 66, 6421-6431]. The raw data (generated using RG_U34A array) was obtained online and normalized by RMA using default parameters followed by the selection of differentially expressed genes using SAM (significance analysis of microarray) algorithm [Tusher, et al. (2001) Proc Natl Acad Sci USA 98, 5116-5121]. Differentially expressed genes for each strain was called using p value cutoff 0.05 and the union of these was used defined as “rat differential gene list”. Genes that appeared in both up and down union groups were excluded. Only genes that had homologues in both species were used for comparisons.

Supervised Principal Component Analysis with Randomized Input

Supervised principal component analysis (SPCA) was used for selection of a subset of genes with prognostic value from differentially expressed genes [Tibshirani, R., et al. (2004). Bioinformatics 20:3034-3044]. The training (Wang's) cohort [Wang, Y., et al. (2005) Lancet 365, 671-679] was randomly split after appropriate filtering of patients into training set and testing set of the same size (the same number of individual patients). Traditional PCA uses all genes to identify principal components in an unsupervised way. However, the 1^(st) principal component of unsupervised PCA might not be the projection direction of interested. SPCA in this study finds the principal components using only genes correlated with survival (ex, log rank test p value 0.05 as cutoff using univariate cox regression). The 1st principal component was used to predict the survival outcome. The correlation between a gene and the predicted outcome was used as the importance score to rank genes of importance. Cross-validation was applied to determine cut-off for significance. Genes with importance score higher than this cut-off formed the gene signature. For each random split configuration, a parity signature was obtained using SPCA. To get a robust gene signature, Wang's data was randomly split into training and testing sets 1,000 times and a signature for each configuration was obtained. It was argued that the genes that significantly contribute to breast cancer progression should appear in signatures multiple times more than randomly expected. Those genes whose frequency appearing in signature 5 times higher than random background were chosen as the final parity gene signature.

Prognostic Signature

3,515 genes were identified that were differentially expressed after pregnancy in CD44+ cells at p value cut-off 0.05 using SageExpress pipeline [Wu, Z. J., et al. (2010). Genome Res 20, 1730-1739]. Pregnancy resulted in multifaceted alterations of the mRNA expression levels in cells. Applying univariate Cox regression, 1899 genes were identified to have significant (log rank p value <0.05) correlation with survival in Wang's cohort, among which 441 genes were shown to be differentially expressed after pregnancy (p value <1.75e-10 using hypergeometric distribution for significance test). Those results suggested that the alterations of pregnancy on cell factory are likely associated with carcinogenesis and cancer progression.

In order to elucidate the parity-induced differential genes that were not only expressed together but also correlated with survival (parity-induced breast cancer signature), supervised principal component analysis described above was applied. Simply using univariate cox regression to identify genes correlated with breast cancer as the parity-induced breast cancer signature has the following drawbacks. First, univariate analysis excludes the contributions of other covariates (genes). Thus significant genes in univariate analysis might not be significant when considering other covariates. Second, gene expression often changes in a coherent way such that genes that are functionally related in one or several pathways often show strong correlation in expression levels, which is not captured by univariate analysis. Parity-induced breast cancer signature was obtained using SPCA on up and down genes after pregnancy separately. Wang's cohort was used as the training set and the signatures were validated in three other widely used breast cancer cohorts (NKI, GSE7390 (Transbig), GSE2990 (Tamoxifen) [Desmedt, C., et al. (2007). Clin Cancer Res 13, 3207-3214; Sotiriou, C., et al. (2006) J Natl Cancer Inst 98, 262-272; van de Vijver, M. J et al. (2002) N Engl J Med 347, 1999-2009]. K-mean clustering (k=2) of these signatures separated patients into two groups with significant survival difference.

Norwegian Cohort

GSE18672 cohort [Haakensen, V. D., et al. (2011a) BMC Cancer 11, 332; Haakensen, V. D., et al. (2011b). BMC medical genomics 4, 77] was used to validate the expression patterns of parity-related genes identified in this study. The following criteria were applied for sample selection from this cohort in order to match the samples used in this study: for nulliparous samples—pre-menopausal and age<40; for parous samples—pre-menopausal, number of parity with live birth=2, age<40, age at 1st birth<30. The following procedures were taken to preprocess the public data cohort GSE18672: 1—Missing value estimation using local least squares (R package pacMethods: llsimpute), 2—All genes were centered to zero followed by a loess normalization (R package affy: normalize.loess).

Statistical Analyses

The differences between the percentage of p27+ and Ki67+ cells in the samples from nulliparous and parous women were analyzed by Fisher exact test. The differences between high and low-density samples were analyzed by binomial test. P value of overlap between two groups was obtained by statistical test on hypergeometric distribution. The differences between the percentages of p27+ in the tissue slices experiments were analyzed by t-test, and the differences in BrdU+ cells were analyzed by Fisher exact test.

Kappa Statistics

Kappa statistics are a statistical measure of inter-rater agreement [Cohen, J. (1960). Educat Psych Meas 20, 37-46]. The input for kappa involves a couple of raters or learners, which classify a set of objects into categories. Here, it was used to compare lists of differentially expressed genes for their congruency. Hierarchical clustering of signaling pathways significantly down or upregulated in the four cell types was performed. Distance between two enrichments was assessed using the kappa statistics. Similar to the design in previous publications [Bessarabova, M., et al. (2011) Cancer Res 71, 3471-3481; Huang da, W., et al. (2007) Genome Biol 8, R183; Shi, W., et al. (2010) Pharmacogenomics J 10, 310-323], the value of 1 was assigned to a map if it was significant for an experiment and the value of 0 if the significant enrichment was not observed. Pathways determined to have significant enrichment are referred to herein as “statistically significant pathways.” Kappa value was calculated as

${\kappa = \frac{{\Pr (a)} - {\Pr (e)}}{1 - {\Pr (e)}}},$

where Pr(a) is the relative observed agreement among two enrichments, and Pr(e) is the hypothetical probability of chance agreement, using the observed data to calculate the probabilities of randomly calling maps significant in each experiment. As the higher values of kappa mean better agreement between enrichments and the maximal possible value of kappa is 1, the value (1-K) was used as a distance between two experiments. Average linkage was used to construct cluster dendrogram depicted in FIG. 10.

Generation of SAGEseq, MSDKseq, and ChIPseq Libraries

Detailed protocols for cell purification and the generation of SAGEseq (Serial Analysis of Gene Expression applied to high-throughput sequencing) [Genome Res. 2010 December; 20(12):1730-9. Epub 2010 Nov. 2., Proc Natl Acad Sci USA. 2012 Feb. 21; 109(8):2820-4. Epub 2010 Nov. 22. (http://research4.dfci.harvard.edu/polyaklab/protocols_linkpage.php)], MSDKseq (Methylation-Specific Digital Karyotyping [Hu, M., et al. (2005) Nat Genet 37, 899-905], and ChIPseq (Chromatin Immunoprecipitation applied to high-throughput sequencing) [Maruyama, R. et al. (2011) PLoS genetics 7, e1001369] libraries are posted on the web-site (http://research4.dfci.harvard.edu/polyaklab/protocols_linkpage.php). Genomic data were analyzed as described before [Kowalczyk, A., et al. (2011) J Comput Biol 18, 391-400; Maruyama, R., et al. (2011) supra; Wu, Z. J., et al. (2010) Genome Res 20, 1730-1739].

Integrated View of ChIPseq, SAGEseq, and MSDKseq Data

Differentially Methylated Regions across parity groups were identified using the Poisson margin test [Kowalczyk, A., et al. (2011) supra]. Genes were ordered as a spectrum going from higher in parous to higher in nulliparous, based on p-values. Fisher exact tests were performed using sum of target gene numbers in 1,000-gene window and total count of target genes outside of the window, testing the enrichment of targets inside the windows.

Protein Interactome Analyses

In order to determine overall activation of specific biological functions due to parity in the cell types analyzed, pathway enrichment, network, and protein interactome analyses were performed using the MetaCore platform as described in Bessarabova et al., supra; Ekins, S., et al. (2006) Book Chapter in In High Content Screening (Humana Press), pp. 319-350; Nikolsky, Y., et al. (2009) Methods Mol Biol 563, 177-196).

Nurses' Health Study Data

The Nurses' Health Study (NHS) is a prospective cohort study established in 1976 when 121,700 female registered nurses from across the United States, aged 30-55 years, completed a mailed questionnaire on factors that influence women's health. Follow-up questionnaires have since been sent out every two years to the NHS participants to update exposure information and ascertain non-fatal incident diseases. Incident breast cancer was ascertained by the biennial questionnaire to study participants. For any report of breast cancer, written permission was obtained from participants to review their medical records to confirm the diagnosis and to classify cancers as in situ or invasive, by histological type, size and presence or absence of metastases. Overall, 99% of self-reported breast cancers have been confirmed. To identify breast cancer cases in non-respondents who died, death certificates and medical records for all deceased participants were obtained to ascertain cause of death. This study was approved by the Human Subjects Committee at Brigham and Women's Hospital in Boston, Mass. Breast cancer cases were followed from the date of diagnosis until Jan. 1, 2008 or death, whichever came first. Ascertainment of deaths included reporting by next of kin or postal authorities or searching the National Death Index.

Approximately 98% of deaths in the NHS have been identified by these methods. Cause of death was ascertained from death certificates and physician review of medical records. Information on estrogen receptor (ER) status was extracted from the medical record and pathology reports. If data were missing for ER status, scoring from immunohistochemical staining for ER on 5 μm paraffin sections cut from tissue microarray (TMA) blocks was used [Tamimi, R. M., et al. (2008) Breast Cancer Res 10, R67]. There were 8,055 women with invasive breast cancer diagnosed after return of the 1976 baseline questionnaire through 2006 questionnaire. One woman was excluded due to missing information on parity. Thus, our final analysis included 8,054 women with invasive breast cancer and information on parity. Survival curves were estimated by the Kaplan-Meier method and statistical significance was assessed with the log-rank test. Multivariate cox proportional hazards regression models were used to evaluate the relationship between parity and breast cancer-specific mortality after adjusting for age at diagnosis, aspirin use, date of diagnosis, disease stage, grade, radiation treatment, chemotherapy and hormonal treatment. All analyses were performed using SAS version 9.1. All statistical tests were two sided and P<0.05 was considered statistically significant.

Accession Numbers

Raw data files and methodological details have been submitted to GEO with accession number GSE32017.

Example 2 Parity-Related Differences in Gene Expression in Multiple Cell Types

This example demonstrates the effect parity has on the cellular composition of normal human breast.

To investigate if parity affects the cellular composition of normal human breast, first breast epithelial cells from nulliparous and parous women were analyzed by FACS (fluorescence-activated cell sorting) for cell surface markers previously associated with luminal epithelial (CD24), myoepithelial (CD10), and progenitor features (lin−/CD44+) [Bloushtain-Qimron et al., supra; Mani et al. (2008) Cell 16; 133(4):704-15; Shipitsin et al., (2007) Cancer Cell 11, 259-273]. It was found that CD24+, CD44+, and CD10+ cells represent three distinct cell populations with minimal overlap both in nulliparous and parous tissues. FIG. 1 shows the FACS plot for CD24+ versus CD44+ cells, and it could be seen that there were very few cells that stained positive for both markers. (FIG. 1). Multicolor immunofluorescence analyses was also performed for these three cell surface markers alone or in combinations, and additional known markers for a subset of luminal (GATA3) and myoepithelial (SMA) cells, which further confirmed the identity of the cells. Subsequent FACS analysis of multiple tissue samples showed significant differences in the relative frequency of CD44+ and CD24+ cells between parous and nulliparous samples, whereas the relative frequency of CD10+ cells was essentially the same (FIG. 2). The changes in the relative frequency of CD24+ and CD44+ cells could potentially have been due to the increased number of lobulo-alveolar structures observed in parous women.

To investigate parity-related differences in global gene expression profiles, immuno-magnetic bead purified (Bloushtain-Qimron et al., 2008 supra; Shipitsin et al., 2007, supra) CD24+, CD10+, and CD44+ cells (captured sequentially, thus, CD44+ fraction was CD24-CD10-CD44+, but the CD24+ fraction may have contained some CD24+CD44+ cells), and fibroblast-enriched stroma from multiple nulliparous and parous women were analyzed using SAGEseq (Serial Analysis of Gene Expression applied to high-throughput sequencing). To minimize variability among individuals unrelated to parity status, women were closely matched for age, the number of pregnancies, time at first and since last pregnancy, and ethnicity. The analysis is summarized in Table 3, below, which shows the tissue code, age, parity, ethnicity, and menopausal status of the patient, type of surgery for tissue acquisition, mammographic breast density, cell type analyzed, raw and aligned tag/read counts for Sageseq, MSDKseq, and ChIPseq data below, in which an “x” in qRT-PCR, qMSP, FACS, and IF/IHC (immunofluorescence/immunohistochemistry) columns indicate the use of that sample for the analysis.

The expression of known cell type-specific genes (e.g., luminal cell markers KRT8 and MUC1, myoepithelial cell markers ACTG2 and CNN1, and progenitor cell markers ZEB2 and TWIST1) was consistently observed in each of the three respective epithelial cell types both from nulliparous and parous samples based on SAGEseq confirming the purity and identity of the cells. Comparison of each cell type between nulliparous and parous samples revealed the most pronounced differences in CD44+ cells (FIG. 3 and Table 4, below), where the numbers of significantly (p<0.05) differentially expressed genes and the fold differences were the largest between groups. Tables 4, 5, 6 and 7 list the differentially expressed genes in CD44+, CD24+, CD10+, and stromal breast epithelial cells, respectively, from normal human reduction mammoplasty samples of nulliparous (NP) and parous (P) women. The tables list gene symbols, log transformed normalized tag counts in CD44+, CD24+, CD10+ or stromal breast epithelial cells from nulliparous (columns 2-4) and parous (columns 5-7) with fold change between nulliparous and parous samples (based on average of actual normalized tag count of the three tissues), p-value (<0.05) and gene description.

The degrees of differences were smaller and similar in CD10+ and CD24+ cells, whereas stromal fibroblasts had the fewest differentially expressed genes (Tables 5 and 6). Further examination of parity-related differences in expression patterns using principal component analysis (PCA) confirmed that CD24+ and CD10+ cells and fibroblasts from nulliparous and parous women were similar, whereas CD44+ cells formed very distinct nulliparous and parous clusters (FIGS. 4A and 4B). Interestingly, CD44+ cells from nulliparous women were more similar to CD10+ cells, whereas from parous cases they were more similar to CD24+ cells. This implied a shift from a more basal to a more luminal gene expression pattern in CD44+ cells after parity (FIG. 5).

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To validate differences in gene expression in additional samples and by other methods, quantitative RT-PCR (qRT-PCR) analyses of selected genes were performed using CD44+ cells from multiple nulliparous and parous cases. Despite some interpersonal variability, statistically significant differences between nulliparous and parous groups were detected that overall correlated with SAGEseq data (FIG. 6).

To validate the parity-related gene expression differences in an independent cohort, the levels of the differentially expressed genes (in all cell types or only in CD44+ cells) were analyzed in gene expression data from breast biopsies of a cohort of Norwegian women matched to the nulliparous and parous samples for age (<40) and parity (P2). Clustering analysis using the differentially expressed gene sets divided these samples into a distinct nulliparous (Nulliparous B) and a mixed parous/nulliparous (Nulliparous A) group (FIG. 7). Using genes differentially expressed in all four cell types (i.e., CD24+, CD10+, CD44+ cells, and fibroblasts), combined, or only in CD44+ cells, gave identical results, supporting the hypothesis that changes in CD44+ cells are the most significant and physiologically relevant. Interestingly, the nulliparous samples that formed a distinct cluster (Nulliparous B), or were closer to parous cases (Nulliparous A), displayed significant differences in serum estradiol levels (SEL), with the samples more similar to parous cases having low SEL; all parous samples also had low SEL (FIG. 8). Because these were all premenopausal women and SEL is known to be higher in the luteal phase of the menstrual cycle, when breast epithelial cell proliferation is also higher, these findings implied that breast tissues of nulliparous and parous women may be more distinct in the luteal phase potentially due to differences in the activity of signaling pathways driving cell proliferation or the number of cells that respond to these stimuli.

To strengthen the hypothesis that the parity-associated differences detected in CD44+ cells might be related to subsequent breast cancer risk, the gene expression profiles of CD44+ cells from parous BRCA1 and BRCA2 mutation carriers, whose risk is not decreased by parity, were analyzed. CD44+ cells from parous BRCA1/2 mutation carriers clustered with CD44+ cells from nulliparous controls (FIG. 9A), thereby demonstrating that parity-associated changes observed in control parous women may not occur in these high risk women. The gene expression data in CD10−, CD24−, CD44+ breast epithelial cells from BRCA1 and BRCA2 mutation carriers is shown in Tables 8 and 9, below. Tables 8 and 9 show, from left column to right column, the t-value (t-score), the q-value, which is the smallest FDR (false discovery rate) at which a particular gene would just stay on the list of positives, the p-value, which is the smallest false positive rate (FPR) at which the gene appears positive, and the gene expression in P1, P2, and P3 (samples from three control tissues (CD10−, CD24−, CD44+ breast epithelial cells from parous subjects)), and in BRCA1-N105, BRCA1-N171 and BRCA1-N174 (samples from three BRCA1 mutation carriers) in Table 8 or in BRCA2-N151, BRCA2-N161 and BRCA2-N172 (samples from three BRCA2 mutation carriers). The statistical values t, p, and q are described at http://discover.nci.nih.gov/microarrayAnalysis/Statistical.Tests.jsp.

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To determine if the lack of parity-associated changes in CD44⁺ cells from BRCA1/2 women could be due to differences in the cell populations identified by the three cell surface markers, FACS analysis of multiple tissue samples from control and BRCA1/2 women was performed. The relative frequency of CD44⁺ was slightly higher in control and BRCA1/2 parous compared to nulliparous control samples, which was associated with a slight decrease in the frequency of CD24⁺ cells, whereas the relative frequency of CD10⁺ cells was about the same in all groups (FIG. 9B). The increase in the relative frequency of CD44⁺ to CD24⁺ cells in parous samples could potentially be due to the increased number of lobulo-alveolar relative to ductal structures observed in parous women (FIG. 1), or due to the loss of CD24⁺ cells during involution, or may also reflect the presence of parity-induced stem cells described in murine mammary glands.

Example 3 Biological Pathways and Networks Affected by Parity-Related Gene Expression Changes

This example identifies biological pathways that are activated or repressed by parity.

It was investigated which signaling pathways might be affected by parity-related molecular changes. Early pregnancy specifically decreases the risk of ER+ breast tumors. Differentially expressed genes (Table 4, supra) were explored in CD44+ cells for candidate mediators of this effect. Several genes were identified that can change the response of breast tissue to steroid hormones by altering metabolism (e.g., HSD17B11, HSD17B12, and HSD17B14) or by modulating nuclear receptors (e.g., NCOR1, NCOR2, NCOA4, and NCOA7). Interestingly, androgen receptor (AR) and one of its key targets PSA (KLK3) were highly expressed in nulliparous CD44+ cells, implying active androgen signaling pathway that is decreased following pregnancy. Among genes highly expressed in parous CD44+ cells were a number of known tumor suppressors, such as Hakai/CBLL1, CASP8, SCRIB and LLGL2, and DNA repair-related genes (e.g., PRKDC, FANCB), suggesting that these cells may be more resistant to transformation in parous women.

In order to determine overall activation of specific biological functions due to parity in the cell types analyzed, pathway enrichment, network, and protein interactome analyses were performed using the MetaCore platform. The analyses are summarized in Table 10, below, which contains a full list of enriched GeneGo pathway maps in four different cell types (CD24+, CD44+, CD10+ and stromal fibroblasts) from human breast epithelium from nulliparous and parous subjects. Table 10 contains canonical pathway maps with p-values (<0.05) indicating significance of enrichment for differentially expressed genes upregulated in individual cell types (CD44+, CD24+, CD10+ and stroma) isolated from nulliparous and parous breast tissue, pathway maps, and p-value of enrichment in differentially expressed gene sets from the indicated human cell types from nulliparous and parous women. Table 10 also includes pathways enriched in genes highly expressed in virgin compared to publicly available datasets for parous rats [Blakely et al., supra]. It was found that parity had similar global effects on three of the four cell types analyzed, as pathways built on expression patterns in CD10+ and CD44+ cells and stroma cluster together for parous and nulliparous states (FIG. 10).

TABLE 10 List of Enriched GeneGo Pathway Maps in Four Different Breast Epithelial Cell Types p-values in Nulliparous p-values in Parous Pathway maps CD44+ CD24+ CD10+ Stroma rat CD44+ CD24+ CD10+ Stroma Cytoskeleton remodeling_Cytoskeleton remodeling 1.05E−09 1.79E−04 3.27E−06 9.10E−05 3.77E−04 3.49E−03 0.0256 1.17E−04 Cytoskeleton remodeling_Regulation of actin cytoskeleton by Rho GTPases 1.34E−09 1.17E−02 2.73E−02 9.98E−07 0.00412 7.52E−04 Cytoskeleton remodeling_TGF, WNT and cytoskeletal remodeling 1.88E−09 5.71E−08 1.46E−07 8.12E−04 2.69E−03 6.92E−03 1.92E−02 7.29E−03 2.63E−04 Cell adhesion_Chemokines and adhesion 2.69E−07 3.55E−05 1.03E−05 3.54E−04 3.88E−03 0.0217 2.84E−02 4.53E−02 Cytoskeleton remodeling_Role of PKA in cytoskeleton reorganisation 6.44E−07 1.40E−04 9.01E−05 0.00934 Development_MAG-dependent inhibition of neurite outgrowth 1.54E−06 1.45E−02 3.82E−02 1.71E−02 1.12E−02 0.0318 Role of DNA methylation in progression of multiple myeloma 2.40E−06 7.26E−03 1.50E−03 6.35E−03 0.00478 4.82E−03 Cell adhesion_Histamine H1 receptor signaling in the interruption of cell barrier integrity 3.24E−06 7.62E−06 6.00E−03 0.0205 0.00325 Cell adhesion_Alpha-4 integrins in cell migration and adhesion 3.71E−06 1.02E−02 6.75E−03 7.85E−03 0.0221 0.0334 Stem cells_Response to hypoxia in glioblastoma stem cells 4.22E−06 3.68E−03 Development_WNT signaling pathway. Part 2 5.42E−06 4.58E−03 5.02E−03 1.38E−02 0.00283 6.24E−06 Development_Slit-Robo signaling 6.19E−06 1.32E−04 3.54E−03 8.20E−03 4.54E−03 Cytoskeleton remodeling_Fibronectin-binding integrins in cell motility 8.94E−06 1.17E−03 7.71E−04 8.39E−04 Oxidative phosphorylation 9.31E−06 1.25E−07 5.50E−03 2.34E−13 Cell adhesion_Role of tetraspanins in the integrin-mediated cell adhesion 1.02E−05 5.25E−04 4.99E−05 Cell cycle_Role of Nek in cell cycle regulation 1.27E−05 7.84E−03 9.44E−04 1.60E−05 5.46E−03 0.0196 Signal transduction_PKA signaling 1.64E−05 1.47E−02 2.59E−03 3.46E−02 0.0356 Blood coagulation_Blood coagulation 1.86E−05 6.50E−04 2.90E−03 Cell adhesion_ECM remodeling 2.09E−05 2.54E−08 1.01E−06 2.90E−03 0.0000897 Inhibitory action of Lipoxin A4 on PDGF, EGF and LTD4 signaling 2.45E−05 4.38E−02 6.75E−03 3.60E−02 0.00123 Stem cells_WNT/Beta-catenin and NOTCH in induction of osteogenesis 2.48E−05 4.20E−03 0.0118 HIF-1 in gastric cancer 3.00E−05 9.13E−03 1.60E−03 2.68E−02 0.0181 Cell adhesion_Plasmin signaling 3.33E−05 7.32E−07 1.41E−02 0.00805 Development_Lipoxin inhibitory action on PDGF, EGF and LTD4 signaling 3.33E−05 4.80E−02 7.80E−03 3.95E−02 0.00144 Cell adhesion_Integrin-mediated cell adhesion and migration 3.84E−05 1.11E−02 1.02E−02 9.18E−03 1.81E−03 0.000871 Cytoskeleton remodeling_Reverse signaling by ephrin B 5.92E−05 4.20E−03 5.25E−03 Immune response_IL-1 signaling pathway 7.06E−05 1.50E−03 6.35E−03 Cell adhesion_Endothelial cell contacts by junctional mechanisms 7.46E−05 4.30E−04 2.36E−03 Signal transduction_cAMP signaling 7.78E−05 1.87E−02 2.53E−03 0.00751 Regulation of CFTR activity (norm and CF) 7.82E−05 1.98E−02 2.57E−04 3.91E−04 1.08E−03 2.12E−02 1.13E−02 Development_TGF-beta-dependent induction of EMT via RhoA, PI3K and ILK. 1.13E−04 3.86E−04 4.37E−04 2.52E−04 1.40E−03 0.00597 6.19E−03 Role of stellate cells in progression of pancreatic cancer 1.16E−04 9.06E−03 7.55E−06 1.92E−04 1.57E−03 0.00135 Cell cycle_Influence of Ras and Rho proteins on G1/S Transition 1.18E−04 3.51E−05 1.73E−02 3.23E−03 4.07E−02 0.000894 2.90E−02 Stem cells_NOTCH1-induced self-renewal of glioblastoma stem cells 1.30E−04 Stem cells_Pancreatic cancer stem cells in tumor metastasis 1.30E−04 3.68E−03 1.36E−06 0.000276 Tumor-stroma interactions in pancreatic cancer 1.44E−04 5.38E−05 8.16E−04 Stem cells_Regulation of lung epithelial progenitor cell differentiation 1.66E−04 2.88E−05 2.41E−02 LKB1 signaling pathway in lung cancer cells 1.66E−04 9.23E−04 1.33E−02 6.90E−04 6.32E−04 0.000598 Immune response_CCR3 signaling in eosinophils 1.68E−04 3.21E−03 4.15E−02 1.76E−02 1.17E−04 0.000191 Non-genomic signaling of ESR2 (membrane) in lung cancer cells 1.76E−04 4.00E−02 1.81E−03 0.00451 Blood coagulation_GPCRs in platelet aggregation 2.20E−04 2.73E−02 1.18E−03 0.0283 Cytoskeleton remodeling_Role of PDGFs in cell migration 2.55E−04 1.10E−02 0.00146 Stem cells_Role of BMP signaling in embryonic stem cell neural differentiation 2.59E−04 3.54E−03 Development_Hedgehog and PTH signaling pathways in bone and cartilage 3.07E−04 1.71E−02 4.70E−02 0.0316 development Stem cells_Endothelial differentiation during embryonic development 3.25E−04 3.98E−05 3.46E−02 0.0365 3.56E−02 Stem cells_Hedgehog, BMP and Parathyroid hormone in osteogenesis 3.25E−04 5.00E−02 1.41E−02 Dual role of BMP signaling in gastric cancer 3.50E−04 1.57E−02 1.31E−03 2.99E−02 0.0306 4.69E−02 IGF signaling in HCC 3.94E−04 1.61E−02 1.11E−03 1.21E−02 1.08E−04 0.0269 0.0108 Development_EGFR signaling via small GTPases 4.43E−04 3.61E−02 Development_FGF2-dependent induction of EMT 4.46E−04 3.56E−04 5.64E−03 0.0139 0.034 Cell adhesion_Cadherin-mediated cell adhesion 4.72E−04 4.30E−04 4.09E−02 3.07E−04 Stem cells_Differentiation of white adipocytes 4.75E−04 6.82E−04 6.78E−06 Apoptosis and survival_Endoplasmic reticulum stress response pathway 4.75E−04 1.76E−02 0.0419 Development_BMP signaling 5.69E−04 2.45E−02 1.15E−02 0.0202 Development_TGF-beta-dependent induction of EMT via MAPK 6.02E−04 3.70E−02 2.33E−03 3.74E−02 7.44E−03 0.00698 Transcription_ChREBP regulation pathway 6.25E−04 6.76E−03 0.0165 6.22E−03 4.33E−03 Translation_Regulation of translation initiation 6.27E−04 2.05E−02 3.85E−02 0.00155 PGE2 pathways in cancer 6.80E−04 0.0333 Immune response_Antigen presentation by MHC class I 8.21E−04 2.32E−02 3.32E−03 Muscle contraction_Regulation of eNOS activity in endothelial cells 8.47E−04 1.36E−03 2.89E−02 2.39E−03 0.0343 HBV-dependent NF-kB and PI3K/AKT pathways leading to HCC 8.76E−04 1.70E−05 3.43E−02 8.47E−03 0.00814 2.99E−02 IL-6 signaling in multiple myeloma 8.76E−04 1.08E−04 3.71E−02 9.11E−03 0.0291 8.14E−03 5.00E−03 Development_Melanocyte development and pigmentation 8.76E−04 Stem cells_Extraembryonic differentiation of embryonic stem cells 9.09E−04 1.65E−03 Stem cells_Astrocyte differentiation from adult stem cells 9.09E−04 3.09E−02 3.95E−02 Apoptosis and survival_BAD phosphorylation 9.18E−04 5.76E−03 8.02E−04 3.55E−03 3.77E−03 7.83E−04 3.78E−04 Apoptosis and survival_Apoptotic TNF-family pathways 9.18E−04 2.61E−02 3.55E−03 0.00377 1.49E−02 Stem cells_Auditory hair cell differentiation in embryogenesis 1.06E−03 Effect of H. pylori infection on gastric epithelial cells motility 1.12E−03 2.38E−04 5.57E−03 Development_S1P3 receptor signaling pathway 1.12E−03 4.78E−03 1.89E−02 0.0126 Development_Role of IL-8 in angiogenesis 1.12E−03 1.88E−03 2.00E−02 0.0212 Immune response_IL-9 signaling pathway 1.13E−03 1.29E−02 3.44E−02 4.32E−02 0.0291 Transcription_CREB pathway 1.35E−03 2.88E−02 1.07E−03 0.00464 4.78E−03 5.07E−04 Apoptosis and survival_Granzyme A signaling 1.35E−03 2.92E−02 6.98E−04 1.33E−02 0.0136 2.64E−03 Cell adhesion_Gap junctions 1.35E−03 1.67E−02 4.63E−02 6.98E−04 DNA damage_Brca1 as a transcription regulator 1.35E−03 2.92E−02 Stem cells_Early embryonal hypaxial myogenesis 1.40E−03 1.71E−02 Immune response_Oncostatin M signaling via MAPK in human cells 1.40E−03 1.12E−02 4.37E−02 3.16E−02 0.00115 Stem cells_Beta adrenergic receptors in brown adipocyte differentiation 1.40E−03 2.20E−03 1.02E−02 0.0000202 ENaC regulation in airways (normal and CF) 1.48E−03 3.27E−03 4.28E−02 EGFR family signaling in pancreatic cancer 1.49E−03 7.40E−06 4.97E−03 1.91E−03 0.00101 Cell adhesion_Endothelial cell contacts by non-junctional mechanisms 1.52E−03 1.36E−02 2.59E−02 0.0423 Immune response_lnhibitory action of Lipoxins on pro-inflammatory TNF-alpha 1.62E−03 3.14E−02 7.19E−03 4.18E−05 0.000182 5.47E−03 signaling Neurophysiological process_Glutamate regulation of Dopamine D1A receptor 1.62E−03 8.10E−03 6.00E−03 signaling Neurophysiological process_Receptor-mediated axon growth repulsion 1.62E−03 8.10E−03 2.56E−02 2.15E−04 Role of cell adhesion molecules in progression of pancreatic cancer 1.62E−03 7.19E−03 Immune response_Fc gamma R-mediated phagocytosis in macrophages 1.62E−03 8.10E−03 2.48E−02 Neurophysiological process_ACM regulation of nerve impulse 1.93E−03 3.50E−02 2.52E−04 0.0226 Transcription_Transcription regulation of aminoacid metabolism 1.98E−03 G-protein signaling_Regulation of p38 and JNK signaling mediated by G-proteins 2.08E−03 4.65E−02 1.40E−02 0.0105 0.0377 Stem cells_Role of GSK3 beta in cardioprotection against myocardial infarction 2.12E−03 2.17E−02 6.03E−03 0.0196 Development_NOTCH-induced EMT 2.12E−03 HCV-dependent transcription regulation leading to HCC 2.12E−03 6.90E−04 3.16E−02 Regulation of lipid metabolism_Insulin signaling: generic cascades 2.29E−03 7.67E−05 2.94E−04 6.73E−03 0.00698 7.65E−04 Development_PDGF signaling via MAPK cascades 2.29E−03 3.70E−02 0.00664 Transport_Clathrin-coated vesicle cycle 2.30E−03 8.53E−04 1.21E−02 0.00213 Stem cells_Stimulation of differentiation of mouse embryonic fibroblasts into 2.30E−03 3.02E−03 4.60E−03 2.20E−04 0.0000954 adipocytes by extracellular factors Immune response_MIF in innate immunity response 2.50E−03 4.50E−03 0.0425 Development_S1P2 and S1P3 receptors in cell proliferation and differentiation 2.54E−03 1.80E−02 1.46E−02 Reproduction_GnRH signaling 2.61E−03 2.32E−02 0.0225 Regulation of lipid metabolism_Stimulation of Arachidonic acid production by ACM receptors 2.61E−03 2.94E−02 4.48E−02 3.00E−04 Regulation of lipid metabolism_Insulin regulation of glycogen metabolism 2.76E−03 2.25E−02 1.95E−04 1.72E−02 0.0178 2.20E−03 Immune response_Oncostatin M signaling via JAK-Stat in human cells 2.84E−03 3.62E−02 Development_WNT signaling pathway. Part 1. Degradation of beta-catenin in the 2.84E−03 3.70E−05 1.79E−03 3.62E−02 0.0006 absence WNT signaling Development_VEGF-family signaling 3.00E−03 2.88E−05 0.0441 Hypoxia-induced EMT in cancer and fibrosis 3.01E−03 6.83E−04 0.0398 Cell adhesion_Role of CDK5 in cell adhesion 3.01E−03 Immune response_IL-2 activation and signaling pathway 3.17E−03 1.22E−02 3.43E−02 2.91E−02 0.0314 2.99E−02 Mechanisms of drug resistance in multiple myeloma 3.17E−03 1.22E−02 4.27E−02 0.0299 Activation of TGF-beta signaling in pancreatic cancer 3.20E−03 Development_NOTCH1-mediated pathway for NF-KB activity modulation 3.20E−03 0.00103 Regulation of VEGF signaling in pancreatic cancer 3.20E−03 2.01E−03 Possible pathway of TGF-beta 1-dependent inhibition of CFTR expression 3.20E−03 Signal transduction_Erk Interactions: Inhibition of Erk 3.20E−03 1.02E−02 1.40E−03 0.0227 Muscle contraction_ GPCRs in the regulation of smooth muscle tone 3.51E−03 2.31E−04 Stem cells_NOTCH in inhibition of WNT/Beta-catenin-induced osteogenesis 3.56E−03 1.16E−04 Apoptosis and survival_Inhibition of ROS-induced apoptosis by 17beta-estradiol 3.56E−03 Development_TGF-beta receptor signaling 3.70E−03 1.34E−02 4.55E−02 TGF-beta 1-induced transactivation of membrane receptors signaling in HCC 3.70E−03 3.28E−03 1.27E−02 2.30E−03 0.000388 Beta-2 adrenergic-dependent CFTR expression 3.87E−03 Immune response_Oncostatin M signaling via MAPK in mouse cells 3.88E−03 8.88E−03 3.66E−02 0.000851 Role of osteoblasts in bone lesions formation in multiple myeloma 3.88E−03 3.09E−02 2.31E−03 Mechanisms of CAM-DR in multiple myeloma 3.88E−03 4.80E−02 3.95E−02 0.0366 Development_TGF-beta-dependent induction of EMT via SMADs 3.88E−03 7.80E−03 0.000216 2.55E−02 Stem cells_WNT and Notch signaling in early cardiac myogenesis 3.88E−03 7.80E−03 2.55E−02 PI3K signaling in gastric cancer 4.30E−03 3.68E−03 9.62E−04 5.23E−04 6.36E−04 0.00226 2.49E−05 Some pathways of EMT in cancer cells 4.30E−03 7.92E−04 7.66E−05 3.56E−02 0.025 Membrane-bound ESR1: interaction with G-proteins signaling 4.30E−03 1.18E−02 1.10E−02 Cell adhesion_Tight junctions 4.66E−03 2.63E−03 1.00E−02 Cytoskeleton remodeling_Keratin filaments 4.66E−03 1.29E−02 1.90E−03 9.08E−03 5.23E−06 0.000138 IGF-1 signaling in pancreatic cancer 4.66E−03 2.61E−03 8.97E−03 4.32E−02 9.08E−03 0.0291 Stem cells_Dopamine-induced expression of CNTF in adult neurogenesis 4.79E−03 4.63E−02 Cell cycle_Role of 14-3-3 proteins in cell cycle regulation 4.79E−03 1.07E−03 0.00516 Development_Thrombopoetin signaling via JAK-STAT pathway 4.79E−03 Immune response_IL-17 signaling pathways 4.82E−03 3.05E−02 0.00571 7.94E−03 Suppression of TGF-beta signaling in pancreatic cancer 4.93E−03 7.26E−03 G-protein signaling_G-Protein alpha-12 signaling pathway 5.57E−03 1.12E−02 0.0067 Translation _Regulation of EIF4F activity 5.72E−03 1.03E−03 6.82E−04 1.81E−04 0.000894 1.59E−03 G-protein signaling_Regulation of cAMP levels by ACM 5.78E−03 Cell adhesion_Ephrin signaling 5.78E−03 2.52E−04 2.48E−02 G-protein signaling_Cross-talk between Ras-family GTPases 6.08E−03 9.44E−03 Proteolysis_Putative ubiquitin pathway 6.08E−03 8.14E−04 Stem cells_Aberrant Wnt signaling in medulloblastoma stem cells 6.08E−03 2.73E−02 3.07E−03 0.000622 Putative role of Estrogen receptor and Androgen receptor signaling in progression of 6.56E−03 5.08E−03 0.00806 lung cancer ERBB family and HGF signaling in gastric cancer 6.56E−03 1.91E−02 1.47E−03 3.60E−03 4.51E−02 4.53E−02 0.00806 Stem cells_Noncanonical WNT signaling in cardiac myogenesis 6.59E−03 9.53E−05 0.00921 K-RAS signaling in lung cancer 6.72E−03 9.01E−03 8.12E−03 2.20E−02 2.46E−02 2.26E−02 1.66E−02 G-protein signaling_Rap2A regulation pathway 7.03E−03 Transport_Macropinocytosis regulation by growth factors 7.05E−03 2.60E−02 0.000969 Development_EGFR signaling pathway 7.05E−03 7.64E−04 4.84E−04 0.0106 Dual role of TGF-beta 1 in HCC 7.59E−03 1.36E−02 Immune response_IFN alpha/beta signaling pathway 7.59E−03 Development_Glucocorticoid receptor signaling 7.59E−03 2.59E−02 0.00515 Cell adhesion_PLAU signaling 7.76E−03 3.17E−03 2.90E−03 0.0386 0.00839 Transcription_P53 signaling pathway 7.76E−03 7.33E−04 1.05E−02 1.40E−02 0.0377 Stem cells_BMP7 in brown adipocyte differentiation 7.76E−03 3.96E−03 0.0000304 Development_Beta-adrenergic receptors regulation of ERK 7.77E−03 2.93E−02 Role and regulation of Prostaglandin E2 in gastric cancer 7.77E−03 0.0249 Development_Leptin signaling via PI3K-dependent pathway 7.77E−03 3.70E−02 7.44E−03 0.0249 Transport_Alpha-2 adrenergic receptor regulation of ion channels 7.77E−03 3.10E−02 3.74E−02 2.93E−02 Influence of bone marrow cell environment on progression of multiple myeloma 7.77E−03 2.33E−03 1.60E−03 0.00664 Immune response_CD40 signaling 7.95E−03 4.01E−02 4.85E−03 1.61E−03 0.0278 3.47E−03 Muscle contraction_ACM regulation of smooth muscle contraction 8.52E−03 9.93E−04 Stem cells_H3K4 demethylases in stem cell maintenance 8.73E−03 2.17E−02 Development_PDGF signaling via STATs and NF-kB 8.73E−03 1.39E−03 2.96E−02 8.83E−04 0.00354 Muscle contraction_Relaxin signaling pathway 8.94E−03 4.00E−02 0.0265 2.90E−02 1.97E−02 Transition of HCC cells to invasive and migratory phenotype 9.07E−03 1.55E−02 0.0141 4.25E−02 WNT signaling in HCC 9.07E−03 4.50E−03 1.42E−04 4.20E−03 0.0141 1.18E−02 Development_Neurotrophin family signaling 9.07E−03 1.42E−04 0.00934 Ubiquinone metabolism 9.10E−03 8.55E−03 9.27E−08 Immune response_Oncostatin M signaling via JAK-Stat in mouse cells 9.13E−03 2.73E−02 Androgen signaling in HCC 9.13E−03 4.73E−03 Cell cycle_Initiation of mitosis 9.37E−03 2.99E−02 0.0306 4.69E−02 Development_Leptin signaling via JAK/STAT and MAPK cascades 9.37E−03 3.60E−02 Transport_Macropinocytosis 9.84E−03 0.0176 Transport_RAB1A regulation pathway 9.84E−03 Cytoskeleton remodeling_Integrin outside-in signaling 1.02E−02 1.22E−02 1.14E−02 3.14E−04 Influence of multiple myeloma cells on bone marrow stromal cells 1.04E−02 3.98E−02 3.27E−02 0.0196 0.00624 Role of metalloproteases and heparanase in progression of pancreatic cancer 1.04E−02 2.45E−02 Cytoskeleton remodeling_Thyroliberin in cytoskeleton remodeling 1.04E−02 Transport_ACM3 in salivary glands 1.06E−02 1.71E−02 0.0465 Transport_Intracellular cholesterol transport in norm 1.10E−02 2.85E−02 Muscle contraction_Delta-type opioid receptor in smooth muscle contraction 1.14E−02 2.36E−03 G-protein signaling_Ras family GTPases in kinase cascades (scheme) 1.14E−02 0.0348 Development_Alpha-1 adrenergic receptors signaling via cAMP 1.16E−02 HCV-mediated liver damage and predisposition to HCC progression via p53 1.16E−02 5.81E−03 0.0118 wtCFTR and delta508 traffic/Clathrin coated vesicles formation (norm and CF) 1.16E−02 3.71E−02 2.16E−03 0.0228 Apoptosis and survival_HTR1A signaling 1.17E−02 4.65E−02 1.00E−02 3.17E−02 0.0327 2.93E−05 Immune response_Histamine signaling in dendritic cells 1.17E−02 4.65E−02 Development_GM-CSF signaling 1.17E−02 6.92E−04 4.04E−02 3.39E−02 3.27E−02 0.00553 Development_A2B receptor: action via G-protein alpha s 1.17E−02 4.65E−02 4.55E−02 0.00897 3.27E−02 Angiogenesis in HCC 1.17E−02 8.29E−04 Pro-inflammatory action of Gastrin in gastric cancer 1.17E−02 3.28E−03 3.39E−02 2.54E−03 0.0231 Chemoresistance pathways mediated by constitutive activation of PI3K pathway and 1.22E−02 2.41E−02 1.09E−03 8.02E−04 2.20E−05 1.72E−02 7.83E−04 1.15E−02 BCL-2 in small cell lung cancer Oxidative stress_Role of ASK1 under oxidative stress 1.22E−02 0.00528 Stem cells_BMP signaling in cardiac myogenesis 1.22E−02 1.38E−03 Transcription_Role of VDR in regulation of genes involved in osteoporosis 1.23E−02 Stem cells_TNF-alpha, IL-1 alpha and WNT5A-dependent regulation of osteogenesis 1.33E−02 1.47E−02 4.83E−02 2.59E−03 0.0356 0.025 and adipogenesis in mesenchymal stem cells Transcription_Role of Akt in hypoxia induced HIF1 activation 1.38E−02 5.59E−03 2.81E−03 1.49E−03 0.00869 Mitochondrial ketone bodies biosynthesis and metabolism 1.38E−02 4.26E−05 Signal transduction_AKT signaling 1.40E−02 4.91E−06 1.54E−04 2.74E−05 0.00425 1.75E−04 Regulation of beta-adrenergic receptors signaling in pancreatic cancer 1.40E−02 Development_Notch Signaling Pathway 1.40E−02 0.00422 Development_A2A receptor signaling 1.40E−02 1.34E−03 2.07E−02 0.0000291 Development_VEGF signaling and activation 1.40E−02 2.64E−02 2.08E−02 Apoptosis and survival_Anti-apoptotic action of Gastrin 1.40E−02 1.34E−03 4.78E−03 0.000175 Neurophysiological process_Melatonin signaling 1.40E−02 Neurophysiological process_EphB receptors in dendritic spine morphogenesis and 1.43E−02 3.95E−02 synaptogenesis Stem cells_Putative pathways of telomerase regulation in glioblastoma stem cells 1.46E−02 6.24E−05 0.000261 Cytoskeleton remodeling_Role of Activin A in cytoskeleton remodeling 1.46E−02 8.91E−04 Stem cells_H3K36 demethylation in stem cell maintenance 1.46E−02 4.24E−02 0.0142 Development_Beta-adrenergic receptors signaling via cAMP 1.50E−02 1.08E−04 0.0117 6.71E−03 Effect of H. pylori infection on inflammation in gastric epithelial cells 1.54E−02 3.27E−02 0.000141 K-RAS signaling in pancreatic cancer 1.60E−02 0.0179 Development_S1P1 signaling pathway 1.60E−02 5.36E−03 0.0139 Development_Ligand-independent activation of ESR1 and ESR2 1.60E−02 2.88E−02 4.78E−03 1.85E−02 0.0139 CFTR-dependent regulation of ion channels in Airway Epithelium (norm and CF) 1.60E−02 Mechanisms of resistance to EGFR inhibitors in lung cancer 1.60E−02 2.81E−04 2.31E−02 2.11E−04 1.82E−04 0.0179 5.07E−04 Development_Regulation of CDK5 in CNS 1.64E−02 HGF signaling in pancreatic cancer 1.64E−02 6.21E−06 3.32E−03 0.003 4.42E−02 E-cadherin signaling and its regulation in gastric cancer 1.67E−02 4.41E−04 1.96E−03 1.90E−03 0.000034 HBV signaling via protein kinases leading to HCC 1.67E−02 2.61E−03 0.0285 Development_Endothelin-1/EDNRA signaling 1.69E−02 1.76E−02 1.32E−02 2.83E−03 1.34E−02 0.00159 Development_VEGF signaling via VEGFR2 - generic cascades 1.82E−02 3.14E−02 2.56E−02 Immune response_IL-13 signaling via JAK-STAT 1.82E−02 Signal transduction_Calcium signaling 1.82E−02 6.00E−03 Cytoskeleton remodeling_ACM3 and ACM4 in keratinocyte migration 1.92E−02 1.12E−02 Stem cells_Role of Neuregulin 1 and Thymosin beta-4 in myocardium regeneration 1.94E−02 7.11E−05 3.90E−03 0.0484 after infarction Cholesterol and Sphingolipids transport/Distribution to the intracellular membrane 1.94E−02 0.014 compartments (normal and CF) Stem cells_Notch signaling in medulloblastoma stem cells 1.94E−02 Proteolysis_Putative SUMO-1 pathway 1.94E−02 5.10E−03 0.0494 FGF signaling in pancreatic cancer 2.07E−02 2.01E−03 8.12E−03 2.70E−02 0.022 1.22E−03 1.66E−02 Cytoskeleton remodeling_CDC42 in cellular processes 2.18E−02 9.99E−03 0.0194 Transcription_Role of heterochromatin protein 1 (HP1) family in transcriptional 2.18E−02 9.99E−03 4.30E−03 2.59E−03 4.36E−02 0.000604 silencing Immune response_MIF-mediated glucocorticoid regulation 2.18E−02 9.99E−03 Apoptosis and survival_Ceramides signaling pathway 2.21E−02 1.61E−02 1.17E−02 1.96E−03 3.69E−04 9.21E−03 7.51E−03 Cell adhesion_Cell-matrix glycoconjugates 2.21E−02 9.29E−05 6.14E−06 0.0475 Role of histone modificators in progression of multiple myeloma 2.28E−02 5.92E−03 1.33E−02 0.00275 Cytoskeleton remodeling_RalA regulation pathway 2.28E−02 2.92E−02 Muscle contraction_S1P2 receptor-mediated smooth muscle contraction 2.28E−02 2.39E−02 0.0158 EGFR signaling pathway in Lung Cancer 2.33E−02 9.13E−03 Influence of smoking on activation of EGFR signaling in lung cancer cells 2.33E−02 Development_HGF signaling pathway 2.33E−02 3.70E−02 2.33E−03 2.93E−02 0.0268 Cardiac Hypertrophy_NF-AT signaling in Cardiac Hypertrophy 2.33E−02 4.65E−02 2.64E−03 2.98E−02 3.69E−02 0.00119 Immune response_TLR signaling pathways 2.36E−02 3.84E−03 0.00521 Chemotaxis_Leukocyte chemotaxis 2.47E−02 2.83E−02 1.36E−03 4.01E−03 4.21E−04 Cytokine production by Th17 cells in CF 2.52E−02 Development_PACAP signaling in neural cells 2.52E−02 Translation_Regulation of EIF2 activity 2.52E−02 7.33E−04 2.90E−03 0.0386 0.00153 Cytoskeleton remodeling_FAK signaling 2.62E−02 1.67E−03 4.89E−03 0.000356 0.0104 Inhibition of apoptosis in pancreatic cancer 2.62E−02 7.84E−03 4.89E−03 0.0381 Apoptosis and survival_Role of lAP-proteins in apoptosis 2.65E−02 2.66E−02 3.16E−03 0.0246 1.56E−02 Stem cells_Neovascularization of glioblastoma in response to hypoxia 2.65E−02 7.71E−04 Stem cells_Embryonal epaxial myogenesis 2.65E−02 1.32E−03 Inflammatory mechanisms of pancreatic cancerogenesis 2.82E−02 4.84E−02 1.94E−03 0.000647 Sorafenib-induced inhibition of cell proliferation and angiogenesis in HCC 2.84E−02 2.34E−02 IL-1 beta-dependent CFTR expression 2.84E−02 Role of IGH translocations in multiple myeloma 2.87E−02 4.50E−03 1.49E−02 0.0114 0.0141 Development_Role of Activin A in cell differentiation and proliferation 2.87E−02 Stem cells_H3K27 demethylases in differentiation of stem cells 2.87E−02 4.50E−03 4.20E−03 Reproduction_Progesterone-mediated oocyte maturation 2.87E−02 0.0425 Stem cells_Regulation of endothelial progenitor cell differentiation from adult stem 2.90E−02 6.15E−04 0.000215 cells Bacterial infections in CF airways 2.90E−02 Cytokine production by Th17 cells in CF (Mouse model) 2.93E−02 4.32E−02 Development_PEDF signaling 2.93E−02 3.71E−02 Immune response_Bacterial infections in normal airways 2.93E−02 4.27E−02 Apoptosis and survival_Granzyme B signaling 3.06E−02 7.84E−03 2.96E−02 0.00373 0.0274 Stem cells_Cooperation between Hedgehog, IGF-2 and HGF signaling pathways in 3.06E−02 3.61E−02 1.52E−04 0.0274 medulloblastoma stem cells Proteolysis_Role of Parkin in the Ubiquitin-Proteasomal Pathway 3.11E−02 1.10E−02 5.01E−03 0.0101 2.67E−02 Immune response_Immunological synapse formation 3.20E−02 2.83E−02 1.62E−04 Stem cells_Muscle progenitor cell migration in hypaxial myogenesis 3.24E−02 0.0104 Apoptosis and survival_Lymphotoxin-beta receptor signaling 3.24E−02 2.19E−02 4.68E−03 0.0465 Immune response_Gastrin in inflammatory response 3.38E−02 1.49E−03 4.23E−02 0.000434 0.00713 DNA damage_Role of SUMO in p53 regulation 3.50E−02 3.80E−03 4.55E−02 0.00781 Transcription_Transcription factor Tubby signaling pathways 3.50E−02 0.00781 Stem cells_EGF-induced proliferation of Type C cells in SVZ of adult brain 3.51E−02 5.80E−03 1.19E−03 0.0218 Normal and pathological TGF-beta-mediated regulation of cell proliferation 3.51E−02 8.95E−03 0.00624 Chemotaxis_Inhibitory action of lipoxins on IL-8- and Leukotriene B4-induced 3.63E−02 1.10E−02 6.36E−04 0.0109 2.24E−04 neutrophil migration Mucin expression in CF via TLRs, EGFR signaling pathways 3.63E−02 1.47E−02 0.0109 Translation_Insulin regulation of translation 3.64E−02 2.00E−04 4.24E−03 7.48E−04 0.000783 1.15E−02 Immune response_Neurotensin-induced activation of IL-8 in colonocytes 3.64E−02 2.41E−02 4.24E−03 0.0115 Signal transduction_JNK pathway 3.64E−02 2.41E−02 0.0000233 Immune response_IL-23 signaling pathway 3.66E−02 2.99E−02 0.0306 4.69E−02 Cytoskeleton remodeling_Neurofilaments 3.66E−02 2.89E−02 1.97E−03 0.00619 0.0469 Development_Thyroliberin signaling 3.87E−02 Transcription_PPAR Pathway 3.87E−02 0.000148 Apoptosis and survival_Cytoplasmic/mitochondrial transport of proapoptotic proteins 4.00E−02 1.61E−04 0.000178 2.27E−02 Bid, Bmf and Bim Stem cells_Role of PKR1 and ILK in cardiac progenitor cells 4.00E−02 0.0334 2.27E−02 Apoptosis and survival_Role of CDK5 in neuronal death and survival 4.00E−02 4.38E−02 6.75E−03 3.60E−02 3.34E−02 5.28E−03 0.0241 Development_CNTF receptor signaling 4.00E−02 3.60E−02 3.34E−02 2.27E−02 0.00463 wtCFTR and deltaF508 traffic/Membrane expression (norm and CF) 4.00E−02 1.28E−02 Chemotaxis_CXCR4 signaling pathway 4.00E−02 6.75E−03 3.60E−02 0.0241 G-protein signaling_Proinsulin C-peptide signaling 4.02E−02 4.45E−06 1.56E−02 1.21E−02 1.17E−02 2.53E−03 7.75E−04 1.42E−03 Apoptosis and survival_TNFR1 signaling pathway 4.08E−02 6.48E−03 1.61E−02 0.0189 1.66E−02 Immune response_IL-10 signaling pathway 4.26E−02 9.09E−03 2.36E−03 3.41E−02 0.0348 Neurophysiological process_Dopamine D2 receptor transactivation of PDGFR in CNS 4.26E−02 1.80E−02 4.09E−02 1.46E−02 2.13E−03 3.48E−02 0.00136 Stem cells_Insulin, IGF-1 and TNF-alpha in brown adipocyte differentiation 4.43E−02 1.26E−04 0.0129 2.83E−03 9.70E−08 Development_Angiopoietin - Tie2 signaling 4.53E−02 1.15E−02 3.09E−02 3.95E−02 0.00118 0.00805 Anti-apoptotic action of Gastrin in pancreatic cancer 4.53E−02 1.15E−02 8.88E−03 5.92E−03 3.66E−02 6.12E−03 2.65E−02 Development_Regulation of telomere length and cellular immortalization 4.53E−02 8.88E−03 2.48E−02 0.0255 Development_Flt3 signaling 4.55E−02 2.88E−02 6.34E−03 2.27E−02 1.79E−02 2.07E−02 1.08E−03 2.89E−03 Pancreatic cancer cell resistance to Tarceva (erlotinib) 4.91E−02 2.05E−02 4.61E−02 2.81E−03 0.0385 0.00254 Immune response_Signaling pathway mediated by IL-6 and IL-1 4.91E−02 Apoptosis and survival_FAS signaling cascades 2.64E−02 2.74E−05 0.000131 4.22E−03 TIP metabolism 0.0000608 Resistance of pancreatic cancer cells to death receptor signaling 1.29E−04 0.00105 4.53E−03 Transcription_Assembly of RNA Polymerase II preinitiation complex on TATA-less 0.000136 0.0257 promoters Development_PIP3 signaling in cardiac myocytes 2.29E−03 4.70E−05 3.38E−04 1.39E−03 6.60E−05 1.24E−04 HCV-dependent regulation of RNA polymerases leading to HCC 0.00035 0.0387 Stem cells_H3K9 demethylases in pluripotency maintenance of stem cells 4.62E−04 4.36E−02 1.98E−02 3.37E−02 Inhibition of apoptosis in gastric cancer 6.32E−04 0.00333 6.61E−04 Cell cycle_Start of DNA replication in early S phase 3.61E−02 0.00067 0.000883 Apoptosis and survival_Caspase cascade 1.64E−03 0.000816 0.00105 Immune response_BCR pathway 7.76E−04 9.79E−04 1.29E−02 4.15E−03 8.06E−03 Immune response_ICOS pathway in T-helper cell 9.01E−03 1.40E−03 1.40E−03 0.0246 6.19E−03 Cell cycle_The metaphase checkpoint 0.00141 Inhibitory action of Lipoxins on neutrophil migration 1.85E−02 1.46E−03 0.0194 4.90E−04 DNA damage_NHEJ mechanisms of DSBs repair 3.16E−02 1.67E−03 0.0297 1.18E−02 Cytoskeleton remodeling_Alpha-1A adrenergic receptor-dependent inhibition of PI3K 5.17E−04 1.67E−03 2.97E−02 1.18E−02 2.89E−04 Regulation of metabolism_Triiodothyronine and Thyroxine signaling 0.00186 Cell cycle_Chromosome condensation in prometaphase 2.70E−03 0.00000331 Development_IGF-1 receptor signaling 2.47E−05 5.23E−04 2.77E−03 9.87E−03 6.69E−04 2.24E−04 dCTP/dUTP metabolism 0.003 dGTP metabolism 0.00332 Inhibition of RUNX3 signaling in gastric cancer 4.63E−02 0.00336 0.00739 Apoptosis and survival_Beta-2 adrenergic receptor anti-apoptotic action 0.00412 8.69E−03 6.09E−03 Signal transduction_Activin A signaling regulation 1.15E−02 4.38E−03 0.00105 4.53E−03 Stem cells_Fetal brown fat cell differentiation 4.00E−03 0.00447 1.41E−02 8.81E−03 Immune response_CXCR4 signaling via second messenger 4.38E−02 6.75E−03 3.60E−02 5.11E−03 0.00711 5.28E−03 dATP/dITP metabolism 0.00573 Signal transduction_PTEN pathway 2.01E−03 6.69E−03 5.97E−03 0.0246 6.19E−03 Microsatellite instability in gastric cancer 0.00601 0.00177 Inhibition of TGF-beta signaling in gastric cancer 6.01E−03 0.0117 3.06E−02 Immune response_Regulation of T cell function by CTLA-4 3.44E−02 1.90E−03 6.82E−03 1.68E−03 2.85E−02 5.95E−03 DNA damage_DNA-damage-induced responses 4.67E−02 0.00747 0.00337 Stem cells_Self-renewal of adult neural stem cells 0.00756 0.029 Regulation of degradation of deltaF508 CFTR in CF 1.67E−02 8.44E−03 0.00869 Transcription_Sin3 and NuRD in transcription regulation 3.46E−03 0.00892 3.47E−02 Blood coagulation_GPIb-IX-V-dependent platelet activation 0.00952 1.11E−02 Transcription_Receptor-mediated HIF regulation 3.96E−03 1.32E−02 5.03E−04 1.01E−02 0.00238 8.39E−03 Stem cells_Signaling pathways in embryonic hepatocyte maturation 5.00E−02 1.41E−02 1.05E−02 0.0365 3.56E−02 Apoptosis and survival_nAChR in apoptosis inhibition and cell cycle progression 2.61E−02 2.13E−02 1.15E−02 0.0118 Stem cells_Role of growth factors in the maintenance of embryonic stem cell 3.23E−03 0.0129 0.000583 pluripotency Apoptosis and survival_Anti-apoptotic TNFs/NF-kB/Bcl-2 pathway 5.10E−03 1.09E−06 1.29E−02 0.0156 6.61E−04 DNA damage_Role of Brca1 and Brca2 in DNA repair 2.92E−02 0.0133 Translation IL-2 regulation of translation 4.24E−02 3.62E−02 0.0139 3.40E−02 3.60E−03 DNA damage Mismatch repair 0.0139 0.00518 Neurophysiological process_Olfactory transduction 0.0139 DNA damage_Inhibition of telomerase activity and cellular senescence 7.04E−03 3.62E−02 0.0139 Immune response_Role of DAP12 receptors in NK cells 4.91E−02 0.0142 0.0451 Immune response_CD28 signaling 1.17E−03 1.44E−02 1.42E−02 0.0451 1.47E−02 Immune response_PIP3 signaling in B lymphocytes 0.0144 1.72E−02 1.15E−02 Immune response_ETV3 affect on CSF1-promoted macrophage differentiation 0.0152 Blood coagulation_GPVI-dependent platelet activation 1.57E−02 0.0157 0.0482 Inhibition of tumor suppressive pathways in pancreatic cancer 8.43E−03 1.65E−02 0.0387 1.69E−02 Transcription_Ligand-Dependent Transcription of Retinoid-Target genes 0.0196 Development_Thrombopoietin-regulated cell processes 2.48E−02 1.99E−02 0.000252 Role of alpha-6/beta-4 integrins in carcinoma progression 1.77E−03 4.35E−06 0.0199 1.52E−02 Chemotaxis_Lipoxin inhibitory action on fMLP-induced neutrophil chemotaxis 2.10E−04 6.69E−03 2.20E−02 0.0226 3.63E−03 Development_EGFR signaling via PIP3 1.17E−02 0.0226 Stem cells_Differentiation of natural regulatory T cells 0.0248 0.00805 G-protein signaling_S1P2 receptor signaling 8.88E−03 0.0248 Translation_Opioid receptors in regulation of translation 2.61E−02 0.0267 9.24E−04 Transport_RAB3 regulation pathway 0.0271 G-protein signaling_RAC1 in cellular process 2.61E−03 1.00E−02 0.0277 DNA damage_Nucleotide excision repair 0.0277 Immune response_Inhibitory action of lipoxins on superoxide production induced by IL- 3.43E−02 2.91E−02 0.0299 8 and Leukotriene B4 in neutrophils Inhibitory action of Lipoxins on Superoxide production in neutrophils 3.43E−02 2.91E−02 0.0299 wtCFTR and delta508-CFTR traffic/Generic schema (norm and CF) 0.0317 4.59E−07 Apoptosis and survival_DNA-damage-induced apoptosis 2.03E−04 0.0327 0.0155 Apoptosis and survival_NGF signaling pathway 9.09E−03 0.0341 0.0135 Apoptosis and survival_APRIL and BAFF signaling 3.35E−03 3.42E−02 0.00921 Immune response_NFAT in Immune response 5.00E−02 1.10E−02 0.0346 0.00987 Apoptosis and survival_Anti-apoptotic TNFs/NF-kB/IAP pathway 3.70E−03 5.59E−03 3.85E−02 0.0394 Immune response_TCR and CD28 co-stimulation in activation of NF-kB 0.0414 Immune response_Innate Immune response to RNA viral infection 6.39E−03 4.33E−02 0.0102 Immune response_IFN gamma signaling pathway 3.60E−03 0.044 1.77E−03 Immune response_CD16 signaling in NK cells 1.88E−02 2.43E−03 1.37E−02 0.0472 0.0121 Immune response_Delta-type opioid receptor signaling in T-cells 2.13E−02 4.84E−02 0.000367 1.40E−02 Apoptosis and survival_p53-dependent apoptosis 1.14E−05 0.0484 0.00352 Effect of H. pylori infection on apoptosis in gastric epithelial cells 7.92E−04 0.0365 Immune response_Histamine H1 receptor signaling in Immune response 1.11E−02 3.18E−02 0.029 Immune response_IL-4 - antiapoptotic action 5.92E−03 0.0136 0.0158 Development_Angiotensin signaling via PYK2 6.48E−03 2.07E−02 0.00422 0.0126 Development_Alpha-2 adrenergic receptor activation of ERK 3.91E−03 7.77E−03 1.68E−03 0.000168 Immune response_CCR5 signaling in macrophages and T lymphocytes 1.26E−03 0.0212 0.00269 Development_A3 receptor signaling 1.22E−02 0.00222 0.0214 G-protein signaling_N-RAS regulation pathway 8.95E−03 Immune response_Murine NKG2D signaling 4.87E−03 1.89E−02 EML4/ALK fusion protein in nonsmoking-related lung cancer 1.39E−03 2.74E−02 1.78E−02 0.0196 Transcription_NF-kB signaling pathway 1.79E−02 3.76E−03 0.00238 Development_ERBB-family signaling 3.96E−03 0.0105 0.0377 Fructose metabolism 7.89E−03 Apoptosis and survival_Apoptotic Activin A signaling 0.00619 0.0469 Development_EPO-induced Jak-STAT pathway 0.00526 DNA damage_Role of NFBD1 in DNA damage response 1.30E−02 Mechanisms of K-RAS addiction in lung cancer cells 2.92E−02 Development_EDNRB signaling 3.70E−02 0.00979 0.00553 Immune response_Role of the Membrane attack complex in cell survival 0.00528 0.0241 Regulation of lipid metabolism_Insulin regulation of fatty acid methabolism 1.72E−02 5.72E−06 KLF6 and regulation of KLF6 alternative splicing in HCC 1.33E−03 0.00424 0.0379 Development_S1P1 receptor signaling via beta-arrestin 3.03E−02 0.0000769 Cell cycle_Cell cycle (generic schema) 1.13E−03 0.00278 Development_Regulation of epithelial-to-mesenchymal transition (EMT) 1.59E−05 0.000016 Development_S1P4 receptor signaling pathway 9.99E−03 8.03E−03 0.0337 Signal transduction_IP3 signaling 4.32E−02 4.27E−02 3.43E−02 1.78E−03 4.92E−04 0.000988 Development_Endothelin-1/EDNRA transactivation of EGFR 9.01E−03 1.40E−03 0.00619 0.0166 Cell cycle Sister chromatid cohesion 0.0198 Glutathione metabolism/Rodent version 2.16E−02 1.00E−05 Development_Beta-adrenergic receptors transactivation of EGFR 2.32E−03 4.70E−02 3.10E−04 0.000166 Development_ACM2 and ACM4 activation of ERK 2.64E−02 4.78E−03 0.0126 Activation of pro-oncogenic TGF-beta potential in gastric cancer 2.60E−03 2.89E−02 0.0306 Stem cells_FGF10 in development of subcutaneous white adipose tissue in 2.34E−02 2.62E−04 0.0168 embryogenesis G-protein signaling_RhoA regulation pathway 1.02E−02 1.38E−03 3.60E−02 0.0227 Immune response_IL-7 signaling in B lymphocytes 1.89E−02 0.0126 G-protein signaling_Rap2B regulation pathway 4.59E−02 Development_Activation of ERK by Alpha-1 adrenergic receptors 0.0152 EGF- and HGF-dependent stimulation of metastasis in gastric cancer 1.42E−03 4.63E−02 Cell cycle_Spindle assembly and chromosome separation 8.95E−03 3.27E−02 Glycogen metabolism 0.0377 Neurophysiological process_Delta-type opioid receptor in the nervous system 0.0408 Fructose metabolism/Rodent version 1.65E−02 Inhibitory action of Lipoxins and Resolvin E1 on neutrophil functions 4.20E−03 0.0425 Immune response_PGE2 in immune and neuroendocrine system interactions 2.81E−02 Development_Dopamine D2 receptor transactivation of EGFR 3.14E−02 1.10E−02 1.01E−02 2.67E−02 0.0000955 Autophagy_Autophagy 7.84E−03 4.95E−03 Regulation of lipid metabolism_RXR-dependent regulation of lipid metabolism via 0.00264 PPAR, RAR and VDR Development_A1 receptor signaling 4.21E−02 8.94E−04 0.00736 Cell cycle_Role of APC in cell cycle regulation 4.95E−03 Plasminogen activators signaling in pancreatic cancer 1.15E−02 3.95E−02 0.0265 NGF activation of NF-kB 5.10E−03 5.00E−04 9.65E−04 0.0197 2.29E−03 Immune response_IL-15 signaling 4.01E−02 4.33E−02 0.00347 0.00108 Cell cycle_Role of SCF complex in cell cycle regulation 1.14E−05 5.00E−04 Development_Gastrin in differentiation of the gastric mucosa 0.00921 Propionate metabolism p.1 0.0441 Lysine metabolism 1.42E−02 0.00192 CFTR folding and maturation (norm and CF) 5.69E−03 0.00369 Development_Keratinocyte differentiation 1.49E−03 Tryptophan metabolism/Rodent version 4.11E−02 0.00734 G-protein signaling_H-RAS regulation pathway 1.03E−02 Normal wtCFTR traffic/Sorting endosome formation 1.86E−04 1.18E−02 Apoptosis and survival_Regulation of Apoptosis by Mitochondrial Proteins 3.26E−02 0.0246 Immune response_IL-4 signaling pathway 2.88E−02 Development_Cross-talk between VEGF and Angiopoietin 1 signaling pathways 1.80E−02 4.09E−02 Cell cycle_ESR1 regulation of G1/S transition 3.98E−02 1.92E−04 Development_Activation of ERK by Kappa-type opioid receptor 1.29E−02 4.32E−02 4.01E−02 1.46E−03 0.00595 HCV-dependent regulation of membrane receptors signaling in HCC 0.000227 Delta508-CFTR traffic/Sorting endosome formation in CF 8.14E−04 2.31E−02 0.000752 Immune response_IL-13 signaling via PI3K-ERK 1.34E−02 G-protein signaling_G-Protein alpha-i signaling cascades 0.0109 Glycolysis and gluconeogenesis p. 1 9.01E−03 Muscle contraction_Oxytocin signaling in uterus and mammary gland 3.05E−02 2.26E−02 2.33E−02 Development_Delta- and kappa-type opioid receptors signaling via beta-arrestin 2.31E−02 0.000622 0.00609 Glutathione metabolism 1.38E−02 3.85E−06 Regulation of lipid metabolism_PPAR regulation of lipid metabolism 1.30E−04 0.0115 Immune response_PGE2 common pathways 0.0269 Immune response_HTR2A-induced activation of cPLA2 6.48E−03 2.81E−02 0.00257 Mitochondrial unsaturated fatty acid beta-oxidation 6.00E−03 0.0152 Development_Role of HDAC and calcium/calmodulin-dependent kinase (CaMK) in 5.64E−03 3.60E−03 0.00177 control of skeletal myogenesis Development_Growth hormone signaling via PI3K/AKT and MAPK cascades 1.72E−02 3.69E−03 0.0115 Neuropeptide signaling in pancreatic cancer 4.32E−02 Apoptosis and survival_NO synthesis and signaling 0.0162 0.0333 Immune response_IL-15 signaling via JAK-STAT cascade 2.73E−02 Regulation of lipid metabolism_G-alpha(q) regulation of lipid metabolism 0.0432 Neurophysiological process_Long-term depression in cerebellum 4.32E−02 Apoptosis and survival_Anti-apoptotic action of membrane-bound ESR1 4.80E−02 0.00612 Development_Role of CDK5 in neuronal development 1.93E−03 2.76E−02 3.60E−02 3.34E−02 0.00463 Cell cycle_Nucleocytoplasmic transport of CDK/Cyclins 1.75E−03 0.0276 Immune response_IL-5 signalling 6.34E−03 2.27E−02 0.00289 Development_Mu-type opioid receptor signaling 1.61E−02 0.00204 0.00751 Pentose phosphate pathway/Rodent version 0.025 Phenylalanine metabolism 3.99E−02 0.00161 Glycolysis and gluconeogenesis (short map) 1.50E−02 6.87E−04 WNT signaling in gastric cancer 1.96E−03 3.61E−04 0.00908 Stem cells_Transcription factors in segregation of hepatocytic lineage 0.000615 4.59E−04 Development_G-Proteins mediated regulation MAPK-ERK signaling 2.70E−02 0.0166 Development_EPO-induced PI3K/AKT pathway and Ca(2+) influx 2.07E−02 0.00425 Development_Angiotensin activation of Akt 3.41E−02 6.69E−03 0.00363 DNA damage_ATM/ATR regulation of G2/M checkpoint 1.80E−02 Development_SSTR1 in regulation of cell proliferation and migration 0.0494 Cytoskeleton remodeling_ESR1 action on cytoskeleton remodeling and cell migration 4.24E−02 Immune response_TREM1 signaling pathway 2.25E−02 0.00521 Stem cells_FGF signaling in pancreatic and hepatic differentiation of embryonic stem 0.0425 cells Tryptophan metabolism 3.92E−02 0.0069 Triacylglycerol metabolism p.1 2.16E−02 0.0123 G-protein signaling_Rac3 regulation pathway 2.34E−02 Development_Growth hormone signaling via STATs and PLC/IP3 3.09E−02 2.50E−04 Regulation of lipid metabolism_Regulation of fatty acid synthesis: NLTP and EHHADH 0.000219 Oxidative stress_Angiotensin II-induced production of ROS 4.80E−02 3.95E−02 Cholesterol and Sphingolipids transport/Recycling to plasma membrane in lung 2.73E−02 (normal and CF) Development_TGF-beta-induction of EMT via ROS 0.0228 Immune response_IL-22 signaling pathway 5.80E−03 3.27E−02 Cell cycle_Transition and termination of DNA replication 0.00189 Stem cells_FGF2-induced self-renewal of adult neural stem cells 0.0118 0.0408 Regulation of metabolism_Bile acids regulation of glucose and lipid metabolism via 0.0318 FXR Apoptosis and survival_NO signaling in survival 0.00515 0.0423 Signal transduction_Activation of PKC via G-Protein coupled receptor 9.05E−04 2.63E−03 1.08E−02 3.27E−03 0.0269 Development_Hedgehog signaling 3.41E−02 2.02E−03 2.10E−04 0.0246 Development_GDNF family signaling 2.01E−03 0.00619 0.0166 HBV-dependent transcription regulation leading to HCC 0.0469 Butanoate metabolism 3.29E−02 0.0192 Development_ERK5 in cell proliferation and neuronal survival 2.73E−02 Development_FGFR signaling pathway 1.76E−02 5.02E−03 3.23E−03 0.0134 0.029 Multiple Myeloma (general scheme) 0.0297 Development_Angiotensin activation of ERK 3.98E−02 1.19E−03 0.0202 0.00406 Leucune, isoleucine and valine metabolism/Rodent version 0.000262 Development_Mu-type opioid receptor signaling via Beta-arrestin 0.0000955 Immune response_Alternative complement pathway 1.79E−05 Development_Angiotensin signaling via beta-Arrestin 2.89E−02 1.27E−02 0.00619 0.00826 Development_Transactivation of PDGFR in non-neuronal cells by Dopamine D2 7.40E−04 0.00306 receptor Development_Membrane-bound ESR1: interaction with growth factors signaling 2.07E−02 0.00422 Transcription_Androgen Receptor nuclear signaling 3.14E−02 6.99E−03 0.00535 2.05E−02 HBV regulation of DNA repair and apoptosis leading to HCC 1.61E−02 Regulation of lipid metabolism_Regulation of lipid metabolism via LXR, NF-Y and 0.0347 SREBP Immune response_IL-6 signaling pathway 4.25E−02 Immune response_Lectin induced complement pathway 1.46E−04 Arachidonic acid production 4.65E−02 1.27E−02 3.70E−02 0.0231 G-protein signaling_Rap1A regulation pathway 1.98E−02 Stem cells_Dopamine-induced transactivation of EGFR in SVZ neural stem cells 3.26E−02 0.0156 0.0176 Immune response_Fc epsilon RI pathway 5.63E−03 2.08E−02 1.41E−02 4.66E−03 0.00881 FGF signaling in gastric cancer 2.73E−02 0.0489 Development_FGF-family signaling 3.09E−02 0.00805 Fatty Acid Omega Oxidation 0.0241 FGFR3 signaling in multiple myeloma 4.37E−02 0.00115 Development_MicroRNA-dependent inhibition of EMT 0.00468 Cardiac Hypertrophy_Ca(2+)-dependent NF-AT signaling in Cardiac Hypertrophy 1.85E−02 1.85E−02 0.0381 Immune response_Role of integrins in NK cells cytotoxicity 0.0347 Stem cells_MMP-14-induced COX-2 expression in glioblastoma stem cells 2.31E−02 Hedgehog signaling in pancreatic cancer 2.52E−05 Neurophysiological process_GABA-A receptor life cycle 0.00162 HCV-dependent cytoplasmic signaling leading to HCC 4.61E−02 1.67E−02 0.000227 0.000193 Neurophysiological process_NMDA-dependent postsynaptic long-term potentiation in 0.0148 0.0045 CA1 hippocampal neurons Immune response_IL-12 signaling pathway 2.31E−02 0.00424 Stem cells_Scheme: Histone H3 demethylases in stem cells 0.0156 Neurophysiological process_HTR1A receptor signaling in neuronal cells 0.0149 0.0475 Atherosclerosis_Role of ZNF202 in regulation of expression of genes involved in 4.81E−02 2.00E−02 1.97E−04 0.0387 1.69E−02 Atherosclerosis Translation_Non-genomic (rapid) action of Androgen Receptor 4.50E−03 1.41E−02 1.18E−02 0.0408 Immune response_Lipoxins and Resolvin E1 inhibitory action on neutrophil functions 2.31E−03 0.0255 Cell cycle_Regulation of G1/S transition (part 2) 4.30E−04 0.0348 Anti-apoptotic action of Gastrin in gastric cancer 1.15E−02 8.88E−03 0.00612 Development_Activation of astroglial cells proliferation by ACM3 1.64E−03 5.80E−03 6.90E−03 0.0202 GTP metabolism 0.0311 Neurophysiological process_Thyroliberin in cell hyperpolarization and excitability 4.80E−02 Glutathione metabolism/Human version 1.50E−02 4.55E−06 Stem cells_FGF2 signaling during embryonic stem cell differentiation 0.0227 Proliferative action of Gastrin in gastric cancer 4.58E−03 3.65E−05 4.59E−02 0.0421 Cell adhesion_Integrin inside-out signaling 7.04E−03 3.84E−03 1.71E−02 0.0356 Tissue factor signaling in Lung Cancer 3.14E−02 Development_Prolactin receptor signaling 2.63E−02 8.70E−03 2.00E−02 0.0406 Phenylalanine metabolism/Rodent version 3.52E−02 0.00559 Development_SSTR2 in regulation of cell proliferation 0.00705 0.00595 Immune response_CD137 signaling in immune cell 7.26E−03 0.0118 Development_WNT5A signaling 9.01E−03 2.82E−02 0.00597 Translation_Translation regulation by Alpha-1 adrenergic receptors 1.03E−03 1.32E−02 0.0419 0.029 Development_Gastrin in cell growth and proliferation 2.89E−03 3.91E−03 2.69E−02 2.42E−02 2.79E−03 0.00394 Effect of H. pylori infection on gastric epithelial cell proliferation 2.63E−02 2.71E−02 Chemotaxis CCR4-induced leukocyte adhesion 4.63E−02 2.39E−02 GTP-XTP metabolism 8.97E−03 Transcription_Ligand-dependent activation of the ESR1/SP pathway 2.92E−02 Immune response_TLR3 and TLR4 induce TICAM1-specific signaling pathway 4.24E−02 1.58E−02 Development_Delta-type opioid receptor mediated cardioprotection 1.03E−02 4.70E−02 0.00808 0.000166 Development_Mu-type opioid receptor regulation of proliferation 1.89E−02 0.00192 Immune response_IL-12-induced IFN-gamma production 2.63E−03 0.000259 Proliferative action of Gastrin in pancreatic cancer 7.26E−03 2.27E−02 0.00108 Cell cycle_Regulation of G1/S transition (part 1) 3.46E−03 4.22E−02 0.0000516 3.50E−02 Protein folding_Membrane trafficking and signal transduction of G-alpha (i) 1.46E−03 2.97E−02 0.00295 heterotrimeric G-protein Immune response_Classical complement pathway 8.50E−06 Transport_Rab-9 regulation pathway 2.84E−02 5.01E−03 Development_Signaling of Beta-adrenergic receptors via Beta-arrestins 0.00736 Lysine metabolism/Rodent version 4.82E−03 0.00209 G-protein signaling_G-Protein beta/gamma signaling cascades 0.00528 0.0241 Immune response_Sialic-acid receptors (Siglecs) signaling 0.0179 Leucune, isoleucine and valine metabolism. p.2 0.000212 Neurophysiological process_Kappa-type opioid receptor in transmission of nerve 4.63E−02 impulses Stem cells_Scheme: Adult neurogenesis in the Subventricular Zone 1.37E−02 Immune response_MIF-JAB1 signaling 2.14E−03 3.14E−02 Immune response_Function of MEF2 in T lymphocytes 4.65E−02 1.27E−02 3.70E−02 0.00553 Immune response_Human NKG2D signaling 1.17E−02 Aflatoxin B1-dependent induction of HCC 3.98E−02 Neurophysiological process_Role of CDK5 in presynaptic signaling 3.88E−02 0.0442 Stem cells_mGluRS signaling in glioblastoma stem cells 4.28E−02 0.0386 0.0269 G-protein signaling_G-Protein alpha-q signaling cascades 1.02E−02 3.60E−02 0.00103 DNA damage_ATM/ATR regulation of G1/S checkpoint 2.44E−07 0.0178 Pentose phosphate pathway 0.0269 Immune response_MIF - the neuroendocrine-macrophage connector 3.41E−02 2.02E−03 3.50E−02 6.69E−03 0.00597 Immune response_Antiviral actions of Interferons 1.21E−02 Glycolysis and gluconeogenesis p. 2/Human version 2.53E−03 Peroxisomal branched chain fatty acid oxidation 3.41E−02 Regulation of lipid metabolism_Alpha-1 adrenergic receptors signaling via arachidonic 4.49E−02 acid Development_Angiotensin signaling via STATs 1.58E−06 Triacylglycerol metabolism p.2 1.89E−02 Glycolysis and gluconeogenesis p.3/Human version 1.10E−02 Immune response_T cell receptor signaling pathway 2.90E−03 Glycolysis and gluconeogenesis p.3 1.10E−02 2-Naphthylamine and 2-Nitronaphtalene metabolism 3.79E−04 Androstenedione and testosterone biosynthesis and metabolism p.2/Rodent version 1.00E−02 Retinol metabolism/Rodent version 1.47E−02 G-protein signaling_Regulation of CDC42 activity 3.27E−02 Mitochondrial long chain fatty acid beta-oxidation 3.41E−02 Pyruvate metabolism/Rodent version 2.91E−03 Neurophysiological process_Netrin-1 in regulation of axon guidance 6.90E−04 Regulation of lipid metabolism_Regulation of lipid metabolism by niacin and 2.48E−02 isoprenaline Stem cells_Scheme: Osteogenic and adipogenic differentiation of mesenchymal stem 4.11E−02 cells Pyruvate metabolism 9.11E−03 Naphthalene metabolism 2.50E−02 Transcription_Role of AP-1 in regulation of cellular metabolism 1.26E−02 1-Naphthylamine and 1-Nitronaphtalene metabolism 6.00E−03 Muscle contraction_Regulation of eNOS activity in cardiomyocytes 4.91E−02 Retinol metabolism 1.95E−02 Androstenedione and testosterone biosynthesis and metabolism p.2 8.88E−03 Acetaminophen metabolism 3.90E−03 Propionate metabolism p.2 1.70E−02

Furthermore, Table 10 lists only the pathways determined to be upregulated in CD44+ cells from nulliparous women relative to CD44+ cells from parous women, and Table 12 lists the pathways that were significantly upregulated in CD44+, CD24− breast epithelial cells of parous women relative to the same cell type in nulliparous women.

The most significant pathways highly active in parous samples in all of these three cell types included apoptosis, survival, and immune response, whereas stem cells and development-related pathways were enriched only in CD44+ cells from nulliparous women (FIG. 11) and Table 10, above, and Table 12, below). Pathways highly active in parous stroma were enriched in energy metabolism, fatty acid metabolism and adipocyte differentiation from stem cells, which is consistent with adipose tissue development and a decrease in breast density following pregnancy. Table 13, below shows a summary of GeneGo functional enrichment analysis by protein class for differentially expressed genes in CD44+, CD24+, CD10+ and stromal cell types isolated from nulliparous and parous normal human breast. Table 13 indicates the actual and expected number of network objects in the activated dataset for a given protein class, and the ratio of the actual and expected number. In the Table, “n” is the total number of genes in the list, “R” is the number of genes showing the indicated protein class in the background list, “N” is the total number of genes in the background list, the mean value for hypergeometric distribution is calculated by the formula: (n*R/N), the z-score is calculated using the formula: ((r−mean)/sqrt(variance)), and the p-value represents the probability to have the given value of r or higher (or lower for negative z-score). The functional categories of genes affected by parity were similar in all four cell types with receptors and enzymes representing the most enriched groups (FIG. 12 and Table 13).

TABLE 11 Pathways Upregulated in Nulliparous CD44+ Cells Relative to Parous CD44+ Cells P-value in Pathway maps NP CD44+ Cytoskeleton remodeling_Role of PKA in cytoskeleton reorganisation 6.44E−07 Development_MAG-dependent inhibition of neurite outgrowth 1.54E−06 Role of DNA methylation in progression of multiple myeloma 2.40E−06 Cell adhesion_Histamine H1 receptor signaling in the interruption of cell barrier integrity 3.24E−06 Stem cells_Response to hypoxia in glioblastoma stem cells 4.22E−06 Development_WNT signaling pathway. Part 2 5.42E−06 Development_Slit-Robo signaling 6.19E−06 Cytoskeleton remodeling_Fibronectin-binding integrins in cell motility 8.94E−06 Oxidative phosphorylation 9.31E−06 Cell adhesion_Role of tetraspanins in the integrin-mediated cell adhesion 1.02E−05 Cell cycle_Role of Nek in cell cycle regulation 1.27E−05 Blood coagulation_Blood coagulation 1.86E−05 Cell adhesion_ECM remodeling 2.09E−05 Inhibitory action of Lipoxin A4 on PDGF, EGF and LTD4 signaling 2.45E−05 Stem cells_WNT/Beta-catenin and NOTCH in induction of osteogenesis 2.48E−05 HIF-1 in gastric cancer 3.00E−05 Cell adhesion_Plasmin signaling 3.33E−05 Development_Lipoxin inhibitory action on PDGF, EGF and LTD4 signaling 3.33E−05 Cell adhesion_Integrin-mediated cell adhesion and migration 3.84E−05 Cytoskeleton remodeling_Reverse signaling by ephrin B 5.92E−05 Immune response_IL-1 signaling pathway 7.06E−05 Cell adhesion_Endothelial cell contacts by junctional mechanisms 7.46E−05 Signal transduction_cAMP signaling 7.78E−05 Role of stellate cells in progression of pancreatic cancer 1.16E−04 Stem cells_NOTCH1-induced self-renewal of glioblastoma stem cells 1.30E−04 Stem cells_Pancreatic cancer stem cells in tumor metastasis 1.30E−04 Tumor-stroma interactions in pancreatic cancer 1.44E−04 Stem cells_Regulation of lung epithelial progenitor cell differentiation 1.66E−04 LKB1 signaling pathway in lung cancer cells 1.66E−04 Immune response _CCR3 signaling in eosinophils 1.68E−04 Non-genomic signaling of ESR2 (membrane) in lung cancer cells 1.76E−04 Blood coagulation_GPCRs in platelet aggregation 2.20E−04 Cytoskeleton remodeling_Role of PDGFs in cell migration 2.55E−04 Stem cells_Role of BMP signaling in embryonic stem cell neural differentiation 2.59E−04 Development_Hedgehog and PTH signaling pathways in bone and cartilage development 3.07E−04 Stem cells_Hedgehog, BMP and Parathyroid hormone in osteogenesis 3.25E−04 IGF signaling in HCC 3.94E−04 Development_EGFR signaling via small GTPases 4.43E−04 Cell adhesion_Cadherin-mediated cell adhesion 4.72E−04 Stem cells_Differentiation of white adipocytes 4.75E−04 Apoptosis and survival_Endoplasmic reticulum stress response pathway 4.75E−04 Development_BMP signaling 5.69E−04 Development_TGF-beta-dependent induction of EMT via MAPK 6.02E−04 PGE2 pathways in cancer 6.80E−04 Immune response_Antigen presentation by MHC class I 8.21E−04 Muscle contraction_Regulation of eNOS activity in endothelial cells 8.47E−04 Development_Melanocyte development and pigmentation 8.76E−04 Stem cells_Extraembryonic differentiation of embryonic stem cells 9.09E−04 Stem cells_Astrocyte differentiation from adult stem cells 9.09E−04 Stem cells_Auditory hair cell differentiation in embryogenesis 1.06E−03 Effect of H. pylori infection on gastric epithelial cells motility 1.12E−03 Development_S1P3 receptor signaling pathway 1.12E−03 Development_Role of IL-8 in angiogenesis 1.12E−03 Immune response_IL-9 signaling pathway 1.13E−03 Cell adhesion_Gap junctions 1.35E−03 DNA damage_Brca1 as a transcription regulator 1.35E−03 Stem cells_Early embryonal hypaxial myogenesis 1.40E−03 Immune response_Oncostatin M signaling via MAPK in human cells 1.40E−03 Stem cells_Beta adrenergic receptors in brown adipocyte differentiation 1.40E−03 ENaC regulation in airways (normal and CF) 1.48E−03 EGFR family signaling in pancreatic cancer 1.49E−03 Cell adhesion_Endothelial cell contacts by non-junctional mechanisms 1.52E−03 Neurophysiological process_Glutamate regulation of Dopamine D1A receptor signaling 1.62E−03 Neurophysiological process_Receptor-mediated axon growth repulsion 1.62E−03 Role of cell adhesion molecules in progression of pancreatic cancer 1.62E−03 Immune response_Fc gamma R-mediated phagocytosis in macrophages 1.62E−03 Neurophysiological process_ACM regulation of nerve impulse 1.93E−03 Transcription_Transcription regulation of aminoacid metabolism 1.98E−03 G-protein signaling_Regulation of p38 and JNK signaling mediated by G-proteins 2.08E−03 Stem cells_Role of GSK3 beta in cardioprotection against myocardial infarction 2.12E−03 Development_NOTCH-induced EMT 2.12E−03 HCV-dependent transcription regulation leading to HCC 2.12E−03 Development_PDGF signaling via MAPK cascades 2.29E−03 Transport_Clathrin-coated vesicle cycle 2.30E−03 Stem cells_Stimulation of differentiation of mouse embryonic fibroblasts into adipocytes by 2.30E−03 extracellular factors Immune response_MIF in innate immunity response 2.50E−03 Development_S1P2 and S1P3 receptors in cell proliferation and differentiation 2.54E−03 Reproduction_GnRH signaling 2.61E−03 Regulation of lipid metabolism_Stimulation of Arachidonic acid production by ACM receptors 2.61E−03 Immune response_Oncostatin M signaling via JAK-Stat in human cells 2.84E−03 Development_WNT signaling pathway. Part 1. Degradation of beta-catenin in the absence WNT 2.84E−03 signaling Development_VEGF-family signaling 3.00E−03 Hypoxia-induced EMT in cancer and fibrosis 3.01E−03 Cell adhesion_Role of CDK5 in cell adhesion 3.01E−03 Mechanisms of drug resistance in multiple myeloma 3.17E−03 Activation of TGF-beta signaling in pancreatic cancer 3.20E−03 Development_NOTCH1-mediated pathway for NF-KB activity modulation 3.20E−03 Regulation of VEGF signaling in pancreatic cancer 3.20E−03 Possible pathway of TGF-beta 1-dependent inhibition of CFTR expression 3.20E−03 Signal transduction_Erk Interactions: Inhibition of Erk 3.20E−03 Muscle contraction_ GPCRs in the regulation of smooth muscle tone 3.51E−03 Stem cells_NOTCH in inhibition of WNT/Beta-catenin-induced osteogenesis 3.56E−03 Apoptosis and survival_Inhibition of ROS-induced apoptosis by 17beta-estradiol 3.56E−03 Development_TGF-beta receptor signaling 3.70E−03 TGF-beta 1-induced transactivation of membrane receptors signaling in HCC 3.70E−03 Beta-2 adrenergic-dependent CFTR expression 3.87E−03 Immune response_Oncostatin M signaling via MAPK in mouse cells 3.88E−03 Role of osteoblasts in bone lesions formation in multiple myeloma 3.88E−03 Mechanisms of CAM-DR in multiple myeloma 3.88E−03 Development_TGF-beta-dependent induction of EMT via SMADs 3.88E−03 Stem cells_WNT and Notch signaling in early cardiac myogenesis 3.88E−03 Some pathways of EMT in cancer cells 4.30E−03 Membrane-bound ESR1: interaction with G-proteins signaling 4.30E−03 Cell adhesion_Tight junctions 4.66E−03 Cytoskeleton remodeling_Keratin filaments 4.66E−03 IGF-1 signaling in pancreatic cancer 4.66E−03 Stem cells_Dopamine-induced expression of CNTF in adult neurogenesis 4.79E−03 Cell cycle_Role of 14-3-3 proteins in cell cycle regulation 4.79E−03 Development_Thrombopoetin signaling via JAK-STAT pathway 4.79E−03 Immune response_IL-17 signaling pathways 4.82E−03 Suppression of TGF-beta signaling in pancreatic cancer 4.93E−03 G-protein signaling_G-Protein alpha-12 signaling pathway 5.57E−03 G-protein signaling_Regulation of cAMP levels by ACM 5.78E−03 Cell adhesion_Ephrin signaling 5.78E−03 G-protein signaling_Cross-talk between Ras-family GTPases 6.08E−03 Proteolysis_Putative ubiquitin pathway 6.08E−03 Stem cells_Aberrant Wnt signaling in medulloblastoma stem cells 6.08E−03 Putative role of Estrogen receptor and Androgen receptor signaling in progression of lung cancer 6.56E−03 ERBB family and HGF signaling in gastric cancer 6.56E−03 Stem cells_Noncanonical WNT signaling in cardiac myogenesis 6.59E−03 G-protein signaling_Rap2A regulation pathway 7.03E−03 Transport_Macropinocytosis regulation by growth factors 7.05E−03 Development_EGFR signaling pathway 7.05E−03 Dual role of TGF-beta 1 in HCC 7.59E−03 Immune response_IFN alpha/beta signaling pathway 7.59E−03 Development_Glucocorticoid receptor signaling 7.59E−03 Cell adhesion_PLAU signaling 7.76E−03 Transcription_P53 signaling pathway 7.76E−03 Stem cells_BMP7 in brown adipocyte differentiation 7.76E−03 Development_Beta-adrenergic receptors regulation of ERK 7.77E−03 Role and regulation of Prostaglandin E2 in gastric cancer 7.77E−03 Development_Leptin signaling via PI3K-dependent pathway 7.77E−03 Transport_Alpha-2 adrenergic receptor regulation of ion channels 7.77E−03 Influence of bone marrow cell environment on progression of multiple myeloma 7.77E−03 Immune response_CD40 signaling 7.95E−03 Muscle contraction_ACM regulation of smooth muscle contraction 8.52E−03 Stem cells_H3K4 demethylases in stem cell maintenance 8.73E−03 Development_PDGF signaling via STATs and NF-kB 8.73E−03 Transition of HCC cells to invasive and migratory phenotype 9.07E−03 WNT signaling in HCC 9.07E−03 Development_Neurotrophin family signaling 9.07E−03 Ubiquinone metabolism 9.10E−03 Immune response_Oncostatin M signaling via JAK-Stat in mouse cells 9.13E−03 Androgen signaling in HCC 9.13E−03 Development_Leptin signaling via JAK/STAT and MAPK cascades 9.37E−03 Transport_RAB1A regulation pathway 9.84E−03 Cytoskeleton remodeling_Integrin outside-in signaling 1.02E−02 Role of metalloproteases and heparanase in progression of pancreatic cancer 1.04E−02 Cytoskeleton remodeling_Thyroliberin in Cytoskeleton remodeling 1.04E−02 Transport_ACM3 in salivary glands 1.06E−02 Transport_Intracellular cholesterol transport in norm 1.10E−02 Muscle contraction_Delta-type opioid receptor in smooth muscle contraction 1.14E−02 G-protein signaling_Ras family GTPases in kinase cascades (scheme) 1.14E−02 Development_Alpha-1 adrenergic receptors signaling via cAMP 1.16E−02 HCV-mediated liver damage and predisposition to HCC progression via p53 1.16E−02 wtCFTR and delta508 traffic/Clathrin coated vesicles formation (norm and CF) 1.16E−02 Immune response_Histamine signaling in dendritic cells 1.17E−02 Development_GM-CSF signaling 1.17E−02 Development_A2B receptor: action via G-protein alpha s 1.17E−02 Angiogenesis in HCC 1.17E−02 Pro-inflammatory action of Gastrin in gastric cancer 1.17E−02 Oxidative stress_Role of ASK1 under oxidative stress 1.22E−02 Stem cells_BMP signaling in cardiac myogenesis 1.22E−02 Transcription_Role of VDR in regulation of genes involved in osteoporosis 1.23E−02 Stem cells_TNF-alpha, IL-1 alpha and WNT5A-dependent regulation of osteogenesis and 1.33E−02 adipogenesis in mesenchymal stem cells Mitochondrial ketone bodies biosynthesis and metabolism 1.38E−02 Regulation of beta-adrenergic receptors signaling in pancreatic cancer 1.40E−02 Development_Notch Signaling Pathway 1.40E−02 Development_A2A receptor signaling 1.40E−02 Development_VEGF signaling and activation 1.40E−02 Apoptosis and survival_Anti-apoptotic action of Gastrin 1.40E−02 Neurophysiological process_Melatonin signaling 1.40E−02 Neurophysiological process_EphB receptors in dendritic spine morphogenesis and synaptogenesis 1.43E−02 Cytoskeleton remodeling_Role of Activin A in cytoskeleton remodeling 1.46E−02 Stem cells_H3K36 demethylation in stem cell maintenance 1.46E−02 Effect of H. pylori infection on inflammation in gastric epithelial cells 1.54E−02 Development_S1P1 signaling pathway 1.60E−02 Development_Ligand-independent activation of ESR1 and ESR2 1.60E−02 CFTR-dependent regulation of ion channels in Airway Epithelium (norm and CF) 1.60E−02 Mechanisms of resistance to EGFR inhibitors in lung cancer 1.60E−02 Development_Regulation of CDK5 in CNS 1.64E−02 HGF signaling in pancreatic cancer 1.64E−02 E-cadherin signaling and its regulation in gastric cancer 1.67E−02 HBV signaling via protein kinases leading to HCC 1.67E−02 Development_Endothelin-1/EDNRA signaling 1.69E−02 Development_VEGF signaling via VEGFR2 - generic cascades 1.82E−02 Immune response_IL-13 signaling via JAK-STAT 1.82E−02 Signal transduction_Calcium signaling 1.82E−02 Cytoskeleton remodeling_ACM3 and ACM4 in keratinocyte migration 1.92E−02 Cholesterol and Sphingolipids transport/Distribution to the intracellular membrane compartments 1.94E−02 (normal and CF) Stem cells_Notch signaling in medulloblastoma stem cells 1.94E−02 Proteolysis_Putative SUMO-1 pathway 1.94E−02 Transcription_Role of heterochromatin protein 1 (HP1) family in transcriptional silencing 2.18E−02 Immune response_MIF-mediated glucocorticoid regulation 2.18E−02 Cell adhesion_Cell-matrix glycoconjugates 2.21E−02 Cytoskeleton remodeling_RalA regulation pathway 2.28E−02 Muscle contraction_S1P2 receptor-mediated smooth muscle contraction 2.28E−02 EGFR signaling pathway in Lung Cancer 2.33E−02 Influence of smoking on activation of EGFR signaling in lung cancer cells 2.33E−02 Development_HGF signaling pathway 2.33E−02 Cardiac Hypertrophy_NF-AT signaling in Cardiac Hypertrophy 2.33E−02 Immune response_TLR signaling pathways 2.36E−02 Chemotaxis_Leukocyte chemotaxis 2.47E−02 Cytokine production by Th17 cells in CF 2.52E−02 Development_PACAP signaling in neural cells 2.52E−02 Translation _Regulation of EIF2 activity 2.52E−02 Cytoskeleton remodeling_FAK signaling 2.62E−02 Inhibition of apoptosis in pancreatic cancer 2.62E−02 Stem cells_Neovascularization of glioblastoma in response to hypoxia 2.65E−02 Stem cells_Embryonal epaxial myogenesis 2.65E−02 Inflammatory mechanisms of pancreatic cancerogenesis 2.82E−02 Sorafenib-induced inhibition of cell proliferation and angiogenesis in HCC 2.84E−02 IL-1 beta-dependent CFTR expression 2.84E−02 Development_Role of Activin A in cell differentiation and proliferation 2.87E−02 Stem cells_H3K27 demethylases in differentiation of stem cells 2.87E−02 Reproduction_Progesterone-mediated oocyte maturation 2.87E−02 Stem cells_Regulation of endothelial progenitor cell differentiation from adult stem cells 2.90E−02 Bacterial infections in CF airways 2.90E−02 Cytokine production by Th17 cells in CF (Mouse model) 2.93E−02 Development_PEDF signaling 2.93E−02 Immune response_Bacterial infections in normal airways 2.93E−02 Stem cells_Cooperation between Hedgehog, IGF-2 and HGF signaling pathways in medulloblastoma 3.06E−02 stem cells Immune response _Immunological synapse formation 3.20E−02 Stem cells_Muscle progenitor cell migration in hypaxial myogenesis 3.24E−02 Apoptosis and survival_Lymphotoxin-beta receptor signaling 3.24E−02 Immune response_Gastrin in inflammatory response 3.38E−02 Transcription_Transcription factor Tubby signaling pathways 3.50E−02 Stem cells_EGF-induced proliferation of Type C cells in SVZ of adult brain 3.51E−02 Normal and pathological TGF-beta-mediated regulation of cell proliferation 3.51E−02 Mucin expression in CF via TLRs, EGFR signaling pathways 3.63E−02 Immune response_Neurotensin-induced activation of IL-8 in colonocytes 3.64E−02 Signal transduction_JNK pathway 3.64E−02 Cytoskeleton remodeling_Neurofilaments 3.66E−02 Development_Thyroliberin signaling 3.87E−02 Transcription_PPAR Pathway 3.87E−02 Stem cells_Role of PKR1 and ILK in cardiac progenitor cells 4.00E−02 Apoptosis and survival_Role of CDK5 in neuronal death and survival 4.00E−02 Development_CNTF receptor signaling 4.00E−02 wtCFTR and deltaF508 traffic/Membrane expression (norm and CF) 4.00E−02 Chemotaxis_CXCR4 signaling pathway 4.00E−02 Neurophysiological process_Dopamine D2 receptor transactivation of PDGFR in CNS 4.26E−02 Immune response_Signaling pathway mediated by IL-6 and IL-1 4.91E−02 Development_FGF2-dependent induction of EMT 4.46E−04 Transcription_ChREBP regulation pathway 6.25E−04 Regulation of lipid metabolism_Insulin regulation of glycogen metabolism 2.76E−03 Transport_Macropinocytosis 9.84E−03 Regulation of CFTR activity (norm and CF) 7.82E−05 Cell adhesion_Chemokines and adhesion 2.69E−07 Development_TGF-beta-dependent induction of EMT via RhoA, PI3K and ILK. 1.13E−04 K-RAS signaling in lung cancer 6.72E−03 Cell adhesion_Alpha-4 integrins in cell migration and adhesion 3.71E−06 Cytoskeleton remodeling_Cytoskeleton remodeling 1.05E−09 Muscle contraction_Relaxin signaling pathway 8.94E−03 Apoptosis and survival_BAD phosphorylation 9.18E−04 IL-6 signaling in multiple myeloma 8.76E−04 Apoptosis and survival_Apoptotic TNF-family pathways 9.18E−04 Immune response_IL-2 activation and signaling pathway 3.17E−03 Dual role of BMP signaling in gastric cancer 3.50E−04 Cytoskeleton remodeling_Regulation of actin cytoskeleton by Rho GTPases 1.34E−09 Cell cycle_Initiation of mitosis 9.37E−03 Transcription_CREB pathway 1.35E−03 Signal transduction_PKA signaling 1.64E−05 Stem cells_Endothelial differentiation during embryonic development 3.25E−04 Cytoskeleton remodeling_TGF, WNT and cytoskeletal remodeling 1.88E−09 HBV-dependent NF-kB and PI3K/AKT pathways leading to HCC 8.76E−04 Translation _Regulation of translation initiation 6.27E−04 Cell cycle_Influence of Ras and Rho proteins on G1/S Transition 1.18E−04 Apoptosis and survival_Granzyme A signaling 1.35E−03

TABLE 12 Pathways Upregulated in Parous CD44+ Cells Relative to Nulliparous CD44+ Cells P-val in Pathway maps P CD44+ TTP metabolism 0.0000608 Resistance of pancreatic cancer cells to death receptor signaling 1.29E−04 Transcription_Assembly of RNA Polymerase II preinitiation complex on TATA-less promoters 0.000136 Development_PIP3 signaling in cardiac myocytes 3.38E−04 HCV-dependent regulation of RNA polymerases leading to HCC 0.00035 Stem cells_H3K9 demethylases in pluripotency maintenance of stem cells 4.62E−04 Inhibition of apoptosis in gastric cancer 6.32E−04 Cell cycle_Start of DNA replication in early S phase 0.00067 Apoptosis and survival_Caspase cascade 0.000816 Immune response_BCR pathway 9.79E−04 Immune response_ICOS pathway in T-helper cell 1.40E−03 Cell cycle_The metaphase checkpoint 0.00141 Inhibitory action of Lipoxins on neutrophil migration 1.46E−03 Cytoskeleton remodeling_Alpha-1A adrenergic receptor-dependent inhibition of PI3K 1.67E−03 DNA damage_NHEJ mechanisms of DSBs repair 1.67E−03 Regulation of metabolism_Triiodothyronine and Thyroxine signaling 0.00186 Cell cycle_Chromosome condensation in prometaphase 2.70E−03 Development_IGF-1 receptor signaling 2.77E−03 dCTP/dUTP metabolism 0.003 dGTP metabolism 0.00332 Inhibition of RUNX3 signaling in gastric cancer 0.00336 Apoptosis and survival_Beta-2 adrenergic receptor anti-apoptotic action 0.00412 Signal transduction_Activin A signaling regulation 4.38E−03 Stem cells_Fetal brown fat cell differentiation 0.00447 Immune response_CXCR4 signaling via second messenger 5.11E−03 dATP/dITP metabolism 0.00573 Signal transduction_PTEN pathway 5.97E−03 Microsatellite instability in gastric cancer 0.00601 Inhibition of TGF-beta signaling in gastric cancer 6.01E−03 Immune response_Regulation of T cell function by CTLA-4 6.82E−03 DNA damage_DNA-damage-induced responses 0.00747 Stem cells_Self-renewal of adult neural stem cells 0.00756 Regulation of degradation of deltaF508 CFTR in CF 8.44E−03 Transcription_Sin3 and NuRD in transcription regulation 0.00892 Blood coagulation_GPIb-IX-V-dependent platelet activation 0.00952 Transcription_Receptor-mediated HIF regulation 1.01E−02 Stem cells_Signaling pathways in embryonic hepatocyte maturation 1.05E−02 Apoptosis and survival_nAChR in apoptosis inhibition and cell cycle progression 1.15E−02 Apoptosis and survival_Anti-apoptotic TNFs/NF-kB/Bcl-2 pathway 1.29E−02 DNA damage_Role of Brca1 and Brca2 in DNA repair 0.0133 Translation_IL-2 regulation of translation 0.0139 DNA damage_Inhibition of telomerase activity and cellular senescence 0.0139 DNA damage_Mismatch repair 0.0139 Neurophysiological process_Olfactory transduction 0.0139 Immune response_CD28 signaling 1.42E−02 Immune response_Role of DAP12 receptors in NK cells 0.0142 Immune response_PIP3 signaling in B lymphocytes 0.0144 Immune response_ETV3 affect on CSF1-promoted macrophage differentiation 0.0152 Blood coagulation_GPVI-dependent platelet activation 0.0157 Inhibition of tumor suppressive pathways in pancreatic cancer 1.65E−02 Transcription_Ligand-Dependent Transcription of Retinoid-Target genes 0.0196 Role of alpha-6/beta-4 integrins in carcinoma progression 0.0199 Development_Thrombopoietin-regulated cell processes 1.99E−02 Chemotaxis_Lipoxin inhibitory action on fMLP-induced neutrophil chemotaxis 2.20E−02 Development_EGFR signaling via PIP3 0.0226 G-protein signaling_S1P2 receptor signaling 0.0248 Stem cells_Differentiation of natural regulatory T cells 0.0248 Translation_Opioid receptors in regulation of translation 2.61E−02 Transport_RAB3 regulation pathway 0.0271 G-protein signaling_RAC1 in cellular process 0.0277 DNA damage_Nucleotide excision repair 0.0277 Immune response_Inhibitory action of lipoxins on superoxide production induced by IL-8 and 2.91E−02 Leukotriene B4 in neutrophils Inhibitory action of Lipoxins on Superoxide production in neutrophils 2.91E−02 wtCFTR and delta508-CFTR traffic/Generic schema (norm and CF) 0.0317 Apoptosis and survival_DNA-damage-induced apoptosis 0.0327 Apoptosis and survival_NGF signaling pathway 0.0341 Apoptosis and survival_APRIL and BAFF signaling 3.42E−02 Immune response_NFAT in immune response 0.0346 Apoptosis and survival_Anti-apoptotic TNFs/NF-kB/IAP pathway 3.85E−02 Immune response_TCR and CD28 co-stimulation in activation of NF-kB 0.0414 Immune response_Innate immune response to RNA viral infection 4.33E−02 Immune response _IFN gamma signaling pathway 0.044 Immune response_CD16 signaling in NK cells 0.0472 Immune response_Delta-type opioid receptor signaling in T-cells 4.84E−02 Apoptosis and survival_p53-dependent apoptosis 0.0484 Stem cells_Role of growth factors in the maintenance of embryonic stem cell pluripotency 0.0129 Chemoresistance pathways mediated by constitutive activation of PI3K pathway and BCL-2 in 2.20E−05 small cell lung cancer Signal transduction_AKT signaling 2.74E−05 Immune response_Inhibitory action of Lipoxins on pro-inflammatory TNF-alpha signaling 4.18E−05 Apoptosis and survival_Cytoplasmic/mitochondrial transport of proapoptotic proteins Bid, Bmf and 1.61E−04 Bim Translation _Regulation of EIF4F activity 1.81E−04 PI3K signaling in gastric cancer 6.36E−04 Chemotaxis_Inhibitory action of lipoxins on IL-8- and Leukotriene B4-induced neutrophil migration 6.36E−04 Translation_Insulin regulation of translation 7.48E−04 Transcription_Role of Akt in hypoxia induced HIF1 activation 1.49E−03 Apoptosis and survival_Ceramides signaling pathway 1.96E−03 Apoptosis and survival_Role of IAP-proteins in apoptosis 3.16E−03 Proteolysis_Role of Parkin in the Ubiquitin-Proteasomal Pathway 5.01E−03 Anti-apoptotic action of Gastrin in pancreatic cancer 5.92E−03

TABLE 13 GeneGo Functional Enrichment Analysis by Protein Class for Differentially Expressed Genes in CD44+, CD24+, CD10+ and Stromal Breast Epithelial Cell Types Protein class Actual n R N Expected Ratio p-value z-score Protein class enriched in nulliparous CD44+ cells phosphatases 33 2078 230 22651 21.1 1.564 6.690E−03 2.732 ligands 67 2078 507 22651 46.51 1.44 1.524E−03 3.188 kinases 71 2078 650 22651 59.63 1.191 6.960E−02 1.567 transcription 101 2078 951 22651 87.24 1.158 6.627E−02 1.579 factors enzymes 286 2078 2693 22651 247.1 1.158 3.576E−03 2.77 proteases 57 2078 552 22651 50.64 1.126 1.896E−01 0.9493 receptors 97 2078 1492 22651 136.9 0.7087 6.932E−05 −3.7 other 1374 2078 15628 22651 1434 0.9584 1.705E−03 −2.972 Protein class enriched in nulliparous CD10+ cells proteases 59 1491 552 22651 36.34 1.624 1.665E−04 3.938 ligands 53 1491 507 22651 33.37 1.588 5.912E−04 3.555 enzymes 218 1491 2693 22651 177.3 1.23 5.826E−04 3.372 transcription 68 1491 951 22651 62.6 1.086 2.531E−01 0.7215 factors phosphatases 16 1491 230 22651 15.14 1.057 4.467E−01 0.2299 kinases 43 1491 650 22651 42.79 1.005 5.096E−01 0.03431 receptors 96 1491 1492 22651 98.21 0.9775 4.319E−01 −0.2388 other 946 1491 15628 22651 1029 0.9196 1.294E−06 −4.792 Protein class enriched in nulliparous CD24+ cells phosphatases 23 1273 230 22651 12.93 1.779 5.428E−03 2.899 enzymes 213 1273 2693 22651 151.3 1.407 9.672E−08 5.495 kinases 45 1273 650 22651 36.53 1.232 8.715E−02 1.464 transcription 51 1273 951 22651 53.45 0.9542 3.967E−01 −0.352 factors ligands 25 1273 507 22651 28.49 0.8774 2.859E−01 −0.6814 proteases 27 1273 552 22651 31.02 0.8703 2.598E−01 −0.7526 receptors 46 1273 1492 22651 83.85 0.5486 1.417E−06 −4.402 other 844 1273 15628 22651 878.3 0.9609 1.799E−02 −2.14 Protein class enriched in nulliparous stromal cells ligands 35 770 507 22651 17.24 2.031 6.543E−05 4.403 proteases 38 770 552 22651 18.76 2.025 3.424E−05 4.574 kinases 36 770 650 22651 22.1 1.629 2.994E−03 3.054 transcription 49 770 951 22651 32.33 1.516 2.625E−03 3.048 factors phosphatases 11 770 230 22651 7.819 1.407 1.619E−01 1.163 receptors 53 770 1492 22651 50.72 1.045 3.891E−01 0.3371 enzymes 69 770 2693 22651 91.55 0.7537 4.980E−03 −2.554 other 482 770 15628 22651 531.3 0.9073 7.001E−05 −3.905 Protein class enriched in parous CD44+ cells phosphatases 24 1820 230 22651 18.48 1.299 1.130E−01 1.346 enzymes 280 1820 2693 22651 216.4 1.294 1.994E−06 4.804 kinases 67 1820 650 22651 52.23 1.283 2.106E−02 2.163 transcription 88 1820 951 22651 76.41 1.152 9.018E−02 1.412 factors proteases 39 1820 552 22651 44.35 0.8793 2.234E−01 −0.8485 ligands 35 1820 507 22651 40.74 0.8592 1.949E−01 −0.948 receptors 76 1820 1492 22651 119.9 0.634 3.035E−06 −4.324 other 1215 1820 15628 22651 1256 0.9676 1.720E−02 −2.151 Protein class enriched in parous CD10+ cells enzymes 241 1721 2693 22651 204.6 1.178 3.179E−03 2.819 kinases 58 1721 650 22651 49.39 1.174 1.131E−01 1.294 ligands 41 1721 507 22651 38.52 1.064 3.611E−01 0.4202 phosphatases 17 1721 230 22651 17.48 0.9728 5.164E−01 −0.1189 transcription 65 1721 951 22651 72.26 0.8996 2.004E−01 −0.9072 factors proteases 33 1721 552 22651 41.94 0.7868 8.152E−02 −1.454 receptors 78 1721 1492 22651 113.4 0.6881 1.122E−04 −3.575 other 1193 1721 15628 22651 1187 1.005 3.921E−01 0.3036 Protein class enriched in parous CD24+ cells phosphatases 16 1173 230 22651 11.91 1.343 1.422E−01 1.223 kinases 42 1173 650 22651 33.66 1.248 8.280E−02 1.498 enzymes 170 1173 2693 22651 139.5 1.219 3.280E−03 2.829 transcription 58 1173 951 22651 49.25 1.178 1.104E−01 1.308 factors ligands 28 1173 507 22651 26.26 1.066 3.900E−01 0.3536 proteases 28 1173 552 22651 28.59 0.9795 5.044E−01 −0.1139 receptors 54 1173 1492 22651 77.26 0.6989 2.041E−03 −2.812 other 780 1173 15628 22651 809.3 0.9638 3.152E−02 −1.9 Protein class enriched in parous stromal cells enzymes 228 950 2693 22651 112.9 2.019 1.785E−26 11.78 kinases 35 950 650 22651 27.26 1.284 7.908E−02 1.536 phosphatases 9 950 230 22651 9.646 0.933 5.007E−01 −0.2137 ligands 12 950 507 22651 21.26 0.5643 1.865E−02 −2.076 proteases 13 950 552 22651 23.15 0.5615 1.370E−02 −2.182 transcription 22 950 951 22651 39.89 0.5516 1.014E−03 −2.956 factors receptors 29 950 1492 22651 62.58 0.4634 5.878E−07 −4.487 other 603 950 15628 22651 655.5 0.92 1.188E−04 −3.759 Protein class enrichment for promoter hypermethylation in nulliparous CD44+ cells kinases 37 838 650 22651 24.05 1.539 6.593E−03 2.731 transcription 54 838 951 22651 35.18 1.535 1.240E−03 3.303 factors enzymes 134 838 2693 22651 99.63 1.345 1.970E−04 3.738 proteases 25 838 552 22651 20.42 1.224 1.745E−01 1.045 ligands 20 838 507 22651 18.76 1.066 4.165E−01 0.2958 phosphatases 9 838 230 22651 8.509 1.058 4.798E−01 0.1724 receptors 40 838 1492 22651 55.2 0.7247 1.541E−02 −2.157 other 523 838 15628 22651 578.2 0.9046 2.087E−05 −4.199 Protein class enrichment for promoter hypermethylation in nulliparous CD44+ cells transcription 32 290 951 22651 12.18 2.628 6.665E−07 5.842 factors ligands 10 290 507 22651 6.491 1.541 1.180E−01 1.402 proteases 9 290 552 22651 7.067 1.273 2.774E−01 0.7408 kinases 10 290 650 22651 8.322 1.202 3.222E−01 0.594 enzymes 39 290 2693 22651 34.48 1.131 2.282E−01 0.8256 receptors 20 290 1492 22651 19.1 1.047 4.490E−01 0.2139 phosphatases 2 290 230 22651 2.945 0.6792 4.332E−01 −0.5569 other 170 290 15628 22651 200.1 0.8496 1.099E−04 −3.844 Protein class enrichment for genebody hypermethylation in nulliparous CD44+ cells transcription 31 249 951 22651 10.45 2.965 6.726E−08 6.528 factors phosphatases 4 249 230 22651 2.528 1.582 2.474E−01 0.9354 receptors 18 249 1492 22651 16.4 1.097 3.762E−01 0.4107 ligands 6 249 507 22651 5.573 1.077 4.852E−01 0.1838 kinases 6 249 650 22651 7.145 0.8397 4.249E−01 −0.4372 enzymes 21 249 2693 22651 29.6 0.7094 5.047E−02 −1.694 proteases 4 249 552 22651 6.068 0.6592 2.712E−01 −0.8547 other 160 249 15628 22651 171.8 0.9313 6.111E−02 −1.625 Protein class enrichment for genebody hypermethylation in parous CD44+ cells transcription 20 170 951 22651 7.137 2.802 3.207E−05 4.937 factors phosphatases 4 170 230 22651 1.726 2.317 9.542E−02 1.746 kinases 11 170 650 22651 4.878 2.255 1.018E−02 2.823 proteases 5 170 552 22651 4.143 1.207 3.995E−01 0.4279 enzymes 21 170 2693 22651 20.21 1.039 4.608E−01 0.1876 receptors 9 170 1492 22651 11.2 0.8037 3.107E−01 −0.6821 ligands 3 170 507 22651 3.805 0.7884 4.700E−01 −0.419 other 97 170 15628 22651 117.3 0.827 6.559E−04 −3.377

The analysis was further focused on CD44+ cells, which showed the most pronounced differences between parous and nulliparous states. Pathways highly active in nulliparous samples are related to major developmental and tumorigenic pathways including cytoskeleton remodeling, chemokines and cell adhesion, and WNT signaling (FIG. 13 and Table 10), whereas pathways more active in parous samples include PI3K/AKT signaling and apoptosis (FIG. 14 and Table 10). Importantly, the highest scored pathway for genes highly expressed in nulliparous samples is four orders of magnitude more statistically significant than those for the genes highly expressed in parous samples, suggesting that downregulation of protumorigenic developmental pathways is a prominent feature of CD44+ cells from parous women. Interactome analysis also demonstrated a much larger number of overconnected proteins in nulliparous than in parous state in all four cell types, but particularly in CD44+ cells (FIG. 12). As the relative number of interactions (connectivity) is directly related to the functional activity of the dataset [Nikolsky, Y., et al. (2008) Cancer Res 68, 9532-9540], this result suggested that parous cells are overall substantially less active than nulliparous ones.

Because pregnancy-induced protection against breast cancer is also observed in rodents, it was investigated whether pathways altered by parity are conserved across species. Pathways in CD44+ cells were compared to that generated based on genes differentially expressed between virgin and parous rats [Blakely et al., 2006, supra; D'Cruz, C. M., and Chodosh, L. A. (2006) Cancer Res 66, 6421-6431]. Significant overlap was found between pathways highly active in nulliparous and virgin samples (thus, downregulated in parous), but almost nothing in common was found among those highly active in parous tissues. The top ranked pathways were all related to cytoskeleton remodeling and cell adhesion, known to be highly relevant in stem cells (FIG. 15A and FIG. 15B). Thus, pregnancy appears to induce similar alterations in the mammary epithelium regardless of species. A network built of the common pathways included a complete NOTCH pathway (including NOTCH1 (GenBank Accession no., AB209873, AF308602, AL592301, BC013208), NOTCH1-NICD, ADAM17 (GenBank Accession no., BM725368, BQ186514), gammasecretase complex (PSENEN, GenBank Accession no., AF220053, BQ222622), APH1A (GenBank Accession Nos. BC020590, BI760743, DC365601), and APH1B (GenBank Accession Nos. AC016207, AI693802)), IGF1 (GenBank Accession Nos. AB209184, AC010202), EGF (GenBank Accession No. AC004050, AC005509), CD44 (GenBank Accession No. BC004372), CD9 (GenBank Accession Nos. AI003581, BG291377), and ITGB1 (GenBank Accession Nos. AI261443, BM973433, BX537407) as “triggers” (ligands and receptors), c-Src (GenBank Accession Nos. AF272982, BC051270), PKC (GenBank Accession No. NM_(—)212535), and FAK (GenBank Accession Nos. AB209083, AK304356) as major signaling kinases, and c-Jun (GenBank Accession Nos. BC002646, BC009874), p53 (GenBank Accession No. AK223026, DA453049), SNAIL1 (GenBank Accession Nos. BC012910, DA972913), and LEFT (GenBank Accession Nos. AC097067, AC118062) as transcription factors.

Example 4 Cell Type-Specific Epigenetic Patterns Related to Parity and their Functional Relevance

This example demonstrates that parity has a more pronounced long-term effect on DNA methylation than on H3 lysine 27 trimethylation (K27) patterns.

Reduction of breast cancer risk in postmenopausal women conferred by full-term pregnancy in early adulthood implies the induction of long-lasting changes such as alterations in cell type-specific epigenetic patterns. To investigate this hypothesis, the comprehensive DNA methylation and K27 profiles of CD24+ and CD44+ cells from nulliparous and parous women were analyzed using MSDKseq applied to high-throughput sequencing and ChIPseq, respectively. The data are summarized in Tables 14-17, below.

Comparison of MSDKseq libraries of nulliparous and parous samples within each cell type showed a higher number of significantly (p<0.05) differentially methylated regions (DMRs) in CD44+ cells and, in both cell types, more DMRs were hypermethylated in nulliparous than in parous cells (FIG. 16 and Table 14, below).

To validate differences in DNA methylation in additional samples and by other methods, quantitative methylation-specific PCR (qMSP) analyses of selected genes were performed using CD44+ cells from multiple nulliparous and parous cases. Despite some interpersonal variability, statistically significant differences were detected between nulliparous and parous groups that overall correlated with MSDKseq data (FIG. 6).

In Table 14, genes with DMR (hypermethylated in parous or nulliparous samples) in promoter region or genebody in CD44+ cells are listed. DMR pattern (hypermethylated in which sample in which region), gene symbol, RefSeq ID, gene description, chromosomal location, log 10 p-value (calculated by Poisson margin model), log ratio of averaged nulliparous and parous MSDK-tag counts, scaled MSDK-tag counts, chromosomal position of BssHII recognition sites, and distance between BssHII sites and TSS (plus and minus indicate downstream and upstream of TSS, respectively) are shown. The log 10 p-value and log ratio have a positive or negative sign which indicates DMR is hypermethylated in parous or nulliparous, respectively.

Global associations between differential gene expression and presence of DMRs were analyzed in CD44+ and CD24+ cells, but significant associations were not found, potentially due to the complex relationship between DNA methylation and transcript levels, as DNA methylation can both positively (e.g., in gene body) and negatively (e.g., in promoters) regulate gene expression, depending on the location relative to transcription start site.

The data from the analyses are summarized in Table 15 and Table 16, below, which list genes that are differentially methylated between nulliparous and parous CD44+ and CD24+ cells, respectively, along with SAGEseq, ChIPseq and MSDKseq data for the listed genes. Significant differences in genes enriched for H3K27me3 mark were not detected in CD44+ or CD24+ cells from nulliparous and parous samples. However, genes highly expressed in CD44+ or CD24+ cells from nulliparous women were not K27-enriched in either parous or nulliparous cases, implying the potential lack of their regulation by the PRC2 complex that establishes this histone mark (see, Tables 15 and 16).

Overall it appears that parity may have a more pronounced long-term effect on DNA methylation than on K27 patterns.

To investigate pathways affected by parity-related epigenetic alterations, pathways enriched by genes associated with gene body or promoter DMRs were analyzed in CD44+ cells from nulliparous and parous samples. Very little overlap was found among the four distinct categories (FIG. 17). Relatively few pathways were significantly enriched in both expression and methylation data and most of these were related to development, TGFβ and WNT signaling.

The fraction of transcription factors (TFs) among differentially methylated genes was 2-3 fold higher than expected and what was observed among differentially expressed genes, implying that promoter methylation might be a preferred control mechanism of their expression. Similar to the expression data, DMRs in nulliparous samples had higher numbers of overconnected objects than in parous ones. Gene body DMRs in CD44+ nulliparous cells had the highest number of overconnected objects and transcription factors represented a significant fraction of overconnected objects in promoter hypermethylated DMRs in CD44+ nulliparous cells. Further, Table 17 lists enriched GeneGo pathway maps for differentially methylated regions (DMRs) in promoter (−5 to 2 kb) and gene body (+2 kb to end) in CD44+ cells from human breast epithelium. The table contains canonical pathway maps with p-values (<0.05) indicating significance of enrichment for differentially methylated genes (hypo/hyper methylated) in CD44+ progenitor-enriched cells from nulliparous or parous cases.

Lengthy table referenced here US20150285802A1-20151008-T00008 Please refer to the end of the specification for access instructions.

Lengthy table referenced here US20150285802A1-20151008-T00009 Please refer to the end of the specification for access instructions.

Lengthy table referenced here US20150285802A1-20151008-T00010 Please refer to the end of the specification for access instructions.

Lengthy table referenced here US20150285802A1-20151008-T00011 Please refer to the end of the specification for access instructions.

Example 5 Persistent Parity-Related Decrease of p27+ Cells

This example demonstrates that the number of p27+ and Ki67+ cells are significantly lower in parous than in nulliparous breast tissues.

As discussed in Example CDKN1B encoding for p27, was one of the most significantly differentially expressed genes in CD44⁺ cells from nulliparous and parous (high in nulliparous) and also from control and BRCA1/2 parous tissues (high in BRCA1/2).

The global profiling results were validated in intact breast epithelium at the single cell level using multicolor immunofluorescence assays for the combined detection of CD24, CD44, and top differentially expressed genes. Genes were selected based on significance of difference between nulliparous and parous groups and antibody availability. A marked decrease was found in the expression of p27, Sox17, and Cox2 in parous compared to nulliparous samples. The levels of expression of these markers were lower in breast epithelial cells of parous women compared to nulliparous women (FIG. 18 and FIG. 19).

p27 has been reported to affect the number and proliferation of stem cells and progenitors in several organs. Thus, the decrease of p27+ cells in parous tissues may indicate that the number or proliferative potential of breast epithelial progenitors is decreased. To investigate this issue, immunofluorescence analysis was performed for Ki67, a proliferation marker expressed in cycling cells, alone and in combination with p27. Using this approach it was observed that the number of Ki67+ cells was significantly lower in parous samples and a small subset of cells was Ki67+p27+ (FIG. 19).

The tissue samples used for the global profiling studies above (Example 3) were obtained from premenopausal women, since the protective effects of pregnancy against breast cancer are likely to be established early, even though they are manifested after menopause. However, to confirm that the parity-related differences detected in premenopausal women were maintained and could be detected even after menopause, the expression of p27, Sox17, and Cox2 was analyzed by immunofluorescence and immunohistochemistry in breast tissue samples from postmenopausal women. Although the observed differences between nulliparous and parous postmenopausal samples were less pronounced, the number of p27+ and Ki67+ cells were still significantly lower in parous than in nulliparous tissues (FIG. 20). This observation also suggested that the differences in the number of p27+ and Ki67+ cells between parous and nulliparous tissues in premenopausal women was not likely due to differences in the phase of the menstrual cycle between groups, as postmenopausal tissues showed similar differences for these markers.

Example 6 Link Between Parity-Related Differences and Mammographic Density

This example demonstrates that p27+ cells are a marker of both parity status and mammographic density, and a strong marker for breast cancer risk prediction.

Mammographic density is one of the most significant risk factors for breast cancer, yet its molecular basis is unknown. Mammographic density is higher in nulliparous women and declines after pregnancy, thus, some of the parity-related differences detected may also be linked to differences in mammographic density. To test this hypothesis, the expression levels of p27, Sox17, Cox2, and Ki67 were analyzed in biopsy samples obtained from high and low density areas of the same breast [Lin, et al. (2011) Breast Cancer Res Treat 128, 505-516]. The overall expression of Sox17, Cox2, p27, and Ki67 were not significantly different between low and high-density areas, but the number of p27+ cells was higher in high-density areas (FIG. 21). Thus, the number of p27+ cells is a marker of both parity status and mammographic density, and because both of these are linked to breast cancer risk, it can be used for breast cancer risk prediction.

Example 7 p27⁺ Cells are Quiescent Hormone-Responsive Cells with Progenitor Features

This example demonstrates that a subset of p27+ cells may represent quiescent hormone-responsive progenitors that are the potential cell-of-origin of breast cancer.

The mutually exclusive expression of Ki67 and p27 in breast epithelial cells with their concomitant decrease in parous compared to nulliparous women implied coordinated regulation and that they may represent actively cycling and quiescent cells with proliferative potential, respectively. Ovarian hormones are the best-understood regulators of breast epithelial cell proliferation and also breast cancer risk. Correlating with this, the gene expression data (Example 2) indicated a decrease in androgen receptor (AR) and AR targets in CD44⁺ cells from parous women (Table 4) and prior studies implied a decrease in ER+ breast epithelial cells in parous compared to nulliparous women. To explore the potential hormonal regulation of p27+ breast epithelial cells, the expression of ER, AR, and p27 was analyzed in breast tissue samples from women with varying parity and hormonal status. These included control nulliparous and parous women, BRCA1/2 mutation carriers, breast biopsy tissues from women in early (8-10 weeks) and late (22-26 weeks) stage of pregnancy, and premenopausal women in the follicular and luteal phases of the menstrual cycle or from women undergoing ovarian hyperstimulation prior to oocyte collection for in vitro fertilization (samples are collected at the time of oocyte collection). For each case, multiple different regions of the same slide or breast tissue sample were analyzed in order to minimize differences due to the known tissue heterogeneity even in the same woman. Interestingly, it was found that nearly all p27+ cells were also ER+, and their numbers were the highest in BRCA1/2 mutation carriers and the lowest in biopsy samples from pregnant women and after ovarian hyperstimulation, where both ovarian hormone and hCG (human choriogonadotropin) levels are the highest (FIG. 22). The frequencies of p27+ cells, ER+ cells, and p27+ER+ cells were also higher in control nulliparous compared to parous women and in follicular relative to luteal phase of the menstrual cycle (FIG. 22). Overall similar observations were made for AR (FIG. 23A), although the overlap between p27 and AR was less pronounced compared to that between p27 and ER (FIG. 23B). The high fraction of AR+ cells in BRCA1 mutation carriers is particularly interesting since AR is a genetic modifier of BRCA1-associated breast cancer risk.

To further investigate the relationship between the numbers of p27⁺ cells and ovarian hormone-induced breast epithelial cell proliferation, immunofluorescence analysis for p27 and Ki67 was performed in tissue samples with the highest differences in hormone levels. Correlating with prior data, the frequency of Ki67⁺ cells was the highest in the luteal phase of the menstrual cycle when both estrogen and progesterone levels are high (FIG. 23B). Samples from early pregnancy had a lower fraction of proliferating Ki67⁺ cells and the numbers of these cells was the lowest in the follicular phase. The frequency of p27⁺ cells displayed an inverse correlation with that of Ki67⁺ cells: it was the highest in the follicular phase and lowest in biopsies from oocyte donors (breast tissue biopsies were taken at the time of oocyte collection) (FIG. 23B). Interestingly, a low but detectable fraction of p27⁺ cells was also Ki67⁺ in the luteal phase and early pregnancy, potentially marking proliferating progenitors in early G1 phase of the cell cycle when p27 and Ki67 can overlap. The differences in the frequency of p27⁺ and Ki67⁺ cells between the follicular and luteal phases was less significant in parous compared to nulliparous women in part due to the lower overall fractions of these cells in parous cases (FIG. 23C).

These results show hat a subset of p27⁺ cells represent quiescent hormone-responsive luminal progenitors and that their frequency relates to the risk of breast cancer.

Example 8 Functional Validation of Parity-Related Differences in Signaling Pathways

This example demonstrates that the decreased activity of stem cell-related pathways following pregnancy lead to decreased Ki67+ and p27+ cells in parous women.

Several signaling pathways less active in CD44+ parous cells were related to stem cell maintenance and cell proliferation (FIG. 11). To investigate if inhibition of these pathways affects the number of proliferating cells, normal breast tissues were incubated in a tissue explant culture model with inhibitors or agonists of selected pathways (e.g., cAMP, EGFR, Cox2, Hh, TGFβ, Wnt, and IGFR) for 8-10 days. Inhibitors of irrelevant pathways (e.g., PARP inhibitor) as additional negative controls were also tested. For each case, three different pieces of breast tissue taken from different regions of the same breast were cultured, to minimize variability due to tissue heterogeneity. The number of p27+ cells and cellular proliferation based on bromodeoxyuridine (BrdU) incorporation (marks cells in S phase of the cell cycle) and Ki67 expression (marks cycling cells irrespective of cell cycle phase) was then assessed.

Tissue architecture and cellular viability were maintained and p27+, Ki67+, and BrdU+ cells were detected in all conditions. It was found that inhibition of cAMP, EGFR, Cox2, Hh, and IGFR signaling significantly (p<0.05) decreased the number of cells incorporating BrdU whereas the TGFBR inhibitor had the opposite effect (FIG. 24) Inhibition of EGFR and Cox2, and, to a lesser degree, Wnt and IGFR, decreased the fraction of Ki67+ cells, whereas the frequency of p27+ cells most pronouncedly decreased following IGFR and TGFBR inhibitor treatment. It was also confirmed that the compounds effectively inhibited the activity of the intended pathways (FIG. 25 and FIG. 26) and that the selected pathways were active in p27+ cells.

To determine whether the numbers and the proliferation of p27+ cells are regulated by ER and estrogen signaling, the fraction of p27+ and Ki67+ cells in tissue slices treated with varying concentrations of ovarian hormones or tamoxifen were analyzed. To correlate the tissue slices data with that was observed under physiologic conditions (FIG. 22), estrogen, progesterone, prolactin, and hCG hormone levels that mimic serum levels in the follicular or luteal phases of the menstrual cycle or in mid-pregnancy were used. It was observed that the numbers of p27+ cells were high in sections treated with concentrations of estrogen present in follicular phase and also following tamoxifen treatment, whereas it decreased following IGFR and TGFBR inhibitor treatment (FIG. 24). Cultures incubated with luteal phase and pregnancy level hormones (FIG. 26B and FIG. 26C). These data further demonstrated that a subset of p27+ cells are hormone-responsive luminal progenitors.

Most importantly, the expression of phosphoSmad2 (pSmad2), a key mediator of TGFβ signaling, demonstrated a nearly complete overlap with that of p27, implying that TGFβ is essential for maintaining these cells in quiescent stage possibly via modulating p27 (FIG. 25). These results imply that the decreased activity of these stem cell-related pathways following pregnancy may lead to decreased Ki67+ and p27+ cells in parous women. Furthermore, the data also suggested a direct role for these signaling pathways in regulating breast epithelial cell proliferation where TGFβ acts as a growth inhibitor and the other pathways are mitogenic.

Example 9 Relevance of Parity to Breast Cancer Risk and Prognosis

The present example demonstrates that parity influences both the risk and prognosis of ER+ breast tumors.

Based on the profiling data above (Example 3), it is presently demonstrated that breast epithelial cells with progenitor features are different in nulliparous and parous women. If these cells serve as cell-of-origin for breast cancer then breast tumors developing in parous and nulliparous women might also be different, and this might impact their gene expression profiles and clinical outcome. To test these hypotheses, the effect of parity on breast cancer-specific survival was investigated in the Nurses' Health Study (NHS). Overall, Kaplan Meier curves showed that there was no significant association between parity and breast cancer-specific survival (p=0.29). However, when the analysis was limited to ER+ tumors, it was found that nulliparous women had a suggestive worse survival compared with parous women (FIG. 27). In multivariate analysis there was still a marginally significant association among women with ER+ tumors, with nulliparous women having a nearly 30% increased risk of death from their disease (HR: 1.29, 95% CI: 0.98, 1.70; p=0.06). Assessing associations between age at first pregnancy and number of pregnancies gave similar results. In contrast, among women with ER− tumors, parity was not associated with breast cancer-specific survival (p=0.51). Thus, parity influences both the risk and prognosis of ER+ breast tumors.

Because pregnancy may not induce the same epigenetic and gene expression changes in all women, due to germline variations, it was next investigated if the parity-related gene expression signature (PAGES) in CD44+ cells might be a more useful prognostic marker than parity status alone. Thus, the expression of PAGES was analyzed in public breast cancer gene expression data with clinical outcome. The supervised principle component analysis (SPCA) was applied on one of the cohorts (Wang) as a training set (FIG. 28) to identify the subset of the PAGES with prognostic value followed by validation in three other cohorts (Desmedt et al., supra; Sotiriou et al., supra; van de Vijver et al., supra), the data for which are shown in FIGS. 29A-C( ). In each dataset ER+ tumors, the tumor subtype affected by parity, and cases without systemic therapy were selected in order to avoid differences due to treatment. All patients in the training set had small (<2 cm), lymph node negative tumors at the time of diagnosis. Using this approach, parity/nulliparity-related gene signatures were identified that split patients into two distinct groups with significant survival difference. The genes included in the prognostic signature are summarized in Table 18, which shows the gene symbol, gene description, gene expression pattern (i.e., high in parous and nulliparous samples), and prognostic values (good or bad prognosis) for each of the genes. Interestingly, such prognostic signature was found among genes highly expressed in both nulliparous and parous samples and each set of genes could be further separated into good and bad signatures. These results reflect the complex relationship between pregnancy and breast cancer that involves both protective and tumor-promoting effects.

TABLE 18 Genes Included In Prognostic Parity/Nulliparity Gene Signature Gene Symbol Description Expression Prognosis A2M alpha-2-macroglobulin nulliparous bad ABLIM1 actin binding LIM protein 1 nulliparous bad ADNP activity-dependent neuroprotector homeobox parous bad APPBP2 amyloid beta precursor protein (cytoplasmic tail) binding protein 2 parous bad AQP1 aquaporin 1 (Colton blood group) nulliparous bad ARID5B AT rich interactive domain 5B (MRF1-like) nulliparous bad ASF1B ASF1 anti-silencing function 1 homolog B (S. cerevisiae) parous bad AZGP1 alpha-2-glycoprotein 1, zinc-binding pseudogene 1; alpha-2-glycoprotein 1, zinc- nulliparous bad binding B3GNT2 UDP-GlcNAc: betaGal beta-1,3-N-acetylglucosaminyltransferase 1; UDP- nulliparous bad GlcNAc: betaGal beta-1,3-N-acetylglucosaminyltransferase 2 BACE2 beta-site APP-cleaving enzyme 2 nulliparous bad BIRC5 baculoviral IAP repeat-containing 5 parous bad C11orf60 chromosome 11 open reading frame 60 nulliparous bad C12orf48 chromosome 12 open reading frame 48 parous bad C19orf56 chromosome 19 open reading frame 56 nulliparous bad CCDC101 coiled-coil domain containing 101 nulliparous bad CCL2 chemokine (C-C motif) ligand 2 nulliparous bad CCNI cyclin I nulliparous bad CCT2 chaperonin containing TCP1, subunit 2 (beta) parous bad CD44 CD44 molecule (Indian blood group) nulliparous bad CENPA centromere protein A parous bad CHEK1 CHK1 checkpoint homolog (S. pombe) parous bad CIR1 corepressor interacting with RBPJ nulliparous bad CLPB ClpB caseinolytic peptidase B homolog (E. coli) parous bad CNN3 calponin 3, acidic nulliparous bad CSTB cystatin B (stefin B) nulliparous bad CTDSP1 CTD (carboxy-terminal domain, RNA polymerase II, polypeptide A) small phosphatase nulliparous bad 1 CTDSPL CTD (carboxy-terminal domain, RNA polymerase II, polypeptide A) small phosphatase- nulliparous bad like CTPS CTP synthase parous bad CXCL12 chemokine (C-X-C motif) ligand 12 (stromal cell-derived factor 1) nulliparous bad DARC Duffy blood group, chemokine receptor nulliparous bad DDX39 DEAD (Asp-Glu-Ala-Asp) box polypeptide 39 parous bad DEF6 differentially expressed in FDCP 6 homolog (mouse) nulliparous bad DULLARD dullard homolog (Xenopus laevis) nulliparous bad DUSP4 dual specificity phosphatase 4 nulliparous bad EEF1A2 eukaryotic translation elongation factor 1 alpha 2 parous bad EFNA4 ephrin-A4 nulliparous bad EIF3G eukaryotic translation initiation factor 3, subunit G nulliparous bad F3 coagulation factor III (thromboplastin, tissue factor) nulliparous bad FBLN1 fibulin 1 nulliparous bad FBXO7 F-box protein 7 nulliparous bad FBXW4 F-box and WD repeat domain containing 4 nulliparous bad FLOT1 flotillin 1 nulliparous bad FTO fat mass and obesity associated nulliparous bad GAPVD1 GTPase activating protein and VPS9 domains 1 parous bad GGT5 gamma-glutamyltransferase 5 nulliparous bad GINS1 GINS complex subunit 1 (Psf1 homolog) parous bad GNB2L1 guanine nucleotide binding protein (G protein), beta polypeptide 2-like 1 nulliparous bad GOLM1 golgi membrane protein 1 nulliparous bad GSTK1 glutathione S-transferase kappa 1 nulliparous bad GSTP1 glutathione S-transferase pi 1 nulliparous bad GYPC glycophorin C (Gerbich blood group) nulliparous bad HEATR2 HEAT repeat containing 2 parous bad HIGD2A HIG1 hypoxia inducible domain family, member 2A nulliparous bad HLA-DPA1 major histocompatibility complex, class II, DP alpha 1 nulliparous bad HNRNPA0 heterogeneous nuclear ribonucleoprotein A0 nulliparous bad IGFBP4 insulin-like growth factor binding protein 4 nulliparous bad IMP3 IMPS, U3 small nucleolar ribonucleoprotein, homolog (yeast) nulliparous bad INPP1 inositol polyphosphate-1-phosphatase nulliparous bad ITM2A integral membrane protein 2A nulliparous bad JOSD1 Josephin domain containing 1 nulliparous bad KIAA0101 KIAA0101 parous bad KIAA0406 KIAA0406 parous bad LITAF lipopolysaccharide-induced TNF factor nulliparous bad LRIG1 leucine-rich repeats and immunoglobulin-like domains 1 nulliparous bad LSM2 LSM2 homolog, U6 small nuclear RNA associated (S. cerevisiae) nulliparous bad MCF2L MCF.2 cell line derived transforming sequence-like parous bad MGMT O-6-methylguanine-DNA methyltransferase nulliparous bad MNAT1 menage a trois homolog 1, cyclin H assembly factor (Xenopus laevis) parous bad NAP1L1 nucleosome assembly protein 1-like 1 nulliparous bad NFYC nuclear transcription factor Y, gamma nulliparous bad NUPR1 nuclear protein, transcriptional regulator, 1 nulliparous bad PALM paralemmin nulliparous bad PIK3IP1 phosphoinositide-3-kinase interacting protein 1 nulliparous bad PNRC1 proline-rich nuclear receptor coactivator 1 nulliparous bad POP1 processing of precursor 1, ribonuclease P/MRP subunit (S. cerevisiae) parous bad PPM1D protein phosphatase 1D magnesium-dependent, delta isoform parous bad PRC1 protein regulator of cytokinesis 1 parous bad PSAP prosaposin nulliparous bad PYCRL pyrroline-5-carboxylate reductase-like parous bad RACGAP1 Rac GTPase activating protein 1 pseudogene; Rac GTPase activating protein 1 parous bad RCOR3 REST corepressor 3 nulliparous bad RECQL4 RecQ protein-like 4 parous bad RNF146 ring finger protein 146 nulliparous bad RPL15 ribosomal protein L15 pseudogene 22; ribosomal protein L15 pseudogene 18; nulliparous bad ribosomal protein L15 pseudogene 17; ribosomal protein L15 pseudogene 3; ribosomal protein L15 pseudogene 7; ribosomal protein L15 RPL22 ribosomal protein L22 pseudogene 11; ribosomal protein L22 nulliparous bad RPLP2 ribosomal protein, large, P2 pseudogene 3; ribosomal protein, large, P2 nulliparous bad RPS6KA1 ribosomal protein S6 kinase, 90 kDa, polypeptide 1 nulliparous bad RRP15 ribosomal RNA processing 15 homolog (S. cerevisiae) parous bad SCRIB scribbled homolog (Drosophila) parous bad SEPP1 selenoprotein P, plasma, 1 nulliparous bad SLC17A9 solute carrier family 17, member 9 parous bad SLC25A28 solute carrier family 25, member 28 nulliparous bad SLC25A6 solute carrier family 25 (mitochondrial carrier; adenine nucleotide translocator), nulliparous bad member 6 SLC35B1 solute carrier family 35, member B1 parous bad SPC25 SPC25, NDC80 kinetochore complex component, homolog (S. cerevisiae) parous bad SRGAP2 SLIT-ROBO Rho GTPase activating protein 2 parous bad STMN1 stathmin 1 parous bad SYNGR3 synaptogyrin 3 parous bad TIMM17A translocase of inner mitochondrial membrane 17 homolog A (yeast) parous bad TNFRSF11 tumor necrosis factor receptor superfamily, member 11b nulliparous bad TNNT3 troponin T type 3 (skeletal, fast) nulliparous bad TPT1 similar to tumor protein, translationally-controlled 1; tumor protein, translationally- nulliparous bad controlled 1 TRIP10 thyroid hormone receptor interactor 10 nulliparous bad TSPAN7 tetraspanin 7 nulliparous bad TXNIP thioredoxin interacting protein nulliparous bad UBE3C ubiquitin protein ligase E3C parous bad UCKL1 uridine-cytidine kinase 1-like 1 parous bad USP32 similar to TBC1 domain family, member 3; ubiquitin specific peptidase 32 parous bad YWHAH tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, eta nulliparous bad polypeptide ZC3H3 zinc finger CCCH-type containing 3 parous bad ZFP36L1 zinc finger protein 36, C3H type-like 1 nulliparous bad ZFP36L2 zinc finger protein 36, C3H type-like 2 nulliparous bad ACY1 aminoacylase 1 parous good AGGF1 angiogenic factor with G patch and FHA domains 1 parous good AGK acylglycerol kinase nulliparous good AMIGO2 adhesion molecule with Ig-like domain 2 nulliparous good ANKRD46 ankyrin repeat domain 46 nulliparous good APOD apolipoprotein D parous good APOL1 apolipoprotein L, 1 parous good APOL3 apolipoprotein L, 3 parous good ARHGAP11 Rho GTPase activating protein 11B; Rho GTPase activating protein 11A parous good ATG4B ATG4 autophagy related 4 homolog B (S. cerevisiae) parous good AZIN1 antizyme inhibitor 1 nulliparous good B3GALNT1 beta-1,3-N-acetylgalactosaminyltransferase 1 (globoside blood group) nulliparous good C13orf34 chromosome 13 open reading frame 34 parous good CBX3 similar to chromobox homolog 3; chromobox homolog 3 nulliparous good CD79A CD79a molecule, immunoglobulin-associated alpha parous good CEACAM5 carcinoembryonic antigen-related cell adhesion molecule 5 parous good CHCHD3 coiled-coil-helix-coiled-coil-helix domain containing 3 nulliparous good CNBP CCHC-type zinc finger, nucleic acid binding protein parous good CNIH cornichon homolog (Drosophila) nulliparous good COBRA1 cofactor of BRCA1 nulliparous good COQ2 coenzyme Q2 homolog, prenyltransferase (yeast) nulliparous good COX6A1 cytochrome c oxidase subunit VIa polypeptide 1 nulliparous good CSTF1 cleavage stimulation factor, 3′ pre-RNA, subunit 1, 50 kDa nulliparous good CYC1 cytochrome c-1 nulliparous good DCPS decapping enzyme, scavenger parous good DPM1 dolichyl-phosphate mannosyltransferase polypeptide 1, catalytic subunit nulliparous good DYNLL1 dynein, light chain, LC8-type 1 parous good E2F5 E2F transcription factor 5, p130-binding nulliparous good EFR3A EFR3 homolog A (S. cerevisiae) nulliparous good EIF3J eukaryotic translation initiation factor 3, subunit J parous good ERO1L ERO1-like (S. cerevisiae) nulliparous good FAM164A family with sequence similarity 164, member A nulliparous good FAM55C family with sequence similarity 55, member C parous good FEN1 flap structure-specific endonuclease 1 nulliparous good FLRT3 fibronectin leucine rich transmembrane protein 3 nulliparous good GLG1 golgi apparatus protein 1 parous good GUF1 GUF1 GTPase homolog (S. cerevisiae) parous good HAUS5 HAUS augmin-like complex, subunit 5 parous good HDGFRP3 hepatoma-derived growth factor, related protein 3 nulliparous good HLA-B major histocompatibility complex, class I, C; major histocompatibility complex, class I, B parous good HLA-DOB major histocompatibility complex, class II, DO beta parous good HMGB2 high-mobility group box 2 nulliparous good INPP5D inositol polyphosphate-5-phosphatase, 145 kDa parous good INVS inversin parous good ITCH itchy E3 ubiquitin protein ligase homolog (mouse) parous good KCNG2 potassium voltage-gated channel, subfamily G, member 2 parous good KDELR2 KDEL (Lys-Asp-Glu-Leu) endoplasmic reticulum protein retention receptor 2 nulliparous good KIAA0391 KIAA0391 nulliparous good LAPTM4B lysosomal protein transmembrane 4 beta nulliparous good LARP4 La ribonucleoprotein domain family, member 4 nulliparous good LILRB1 leukocyte immunoglobulin-like receptor, subfamily B (with TM and ITIM domains), parous good member 1 MAP3K7IP mitogen-activated protein kinase kinase kinase 7 interacting protein 1 parous good METT11D1 methyltransferase 11 domain containing 1; similar to methyltransferase 11 domain parous good containing 1 isoform 2 MLLT11 myeloid/lymphoid or mixed-lineage leukemia (trithorax homolog, Drosophila); nulliparous good translocated to, 11 MLX MAX-like protein X parous good MTDH metadherin nulliparous good NDRG4 NDRG family member 4 nulliparous good NDUFA4 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 4, 9 kDa nulliparous good NFS1 NFS1 nitrogen fixation 1 homolog (S. cerevisiae) nulliparous good NRAS neuroblastoma RAS viral (v-ras) oncogene homolog nulliparous good P4HA2 prolyl 4-hydroxylase, alpha polypeptide II nulliparous good PHF1 PHD finger protein 1 parous good PIK3CG phosphoinositide-3-kinase, catalytic, gamma polypeptide parous good PLEKHF2 pleckstrin homology domain containing, family F (with FYVE domain) member 2 nulliparous good PLOD3 procollagen-lysine, 2-oxoglutarate 5-dioxygenase 3 parous good PNP nucleoside phosphorylase nulliparous good PNPLA2 patatin-like phospholipase domain containing 2 parous good PPP1CC protein phosphatase 1, catalytic subunit, gamma isoform nulliparous good PPP3R1 protein phosphatase 3 (formerly 2B), regulatory subunit B, alpha isoform nulliparous good PRPF31 PRP31 pre-mRNA processing factor 31 homolog (S. cerevisiae) nulliparous good PSMA2 proteasome (prosome, macropain) subunit, alpha type, 2 nulliparous good PSMA3 proteasome (prosome, macropain) subunit, alpha type, 3 nulliparous good PSMA4 proteasome (prosome, macropain) subunit, alpha type, 4 nulliparous good PSMA6 proteasome (prosome, macropain) subunit, alpha type, 6 nulliparous good PSMD4 proteasome (prosome, macropain) 26S subunit, non-ATPase, 4 nulliparous good PUF60 poly-U binding splicing factor 60 KDa nulliparous good RALA v-ral simian leukemia viral oncogene homolog A (ras related) nulliparous good RBBP7 retinoblastoma binding protein 7 nulliparous good RFC3 replication factor C (activator 1) 3, 38 kDa nulliparous good RHBDL1 rhomboid, veinlet-like 1 (Drosophila) parous good RINT1 RAD50 interactor 1 parous good RNASEH1 ribonuclease H1 parous good RNF125 ring finger protein 125 parous good RPS11 ribosomal protein S11 pseudogene 5; ribosomal protein S11 parous good RPS6 ribosomal protein S6 pseudogene 25; ribosomal protein S6; ribosomal protein S6 parous good pseudogene 1 RRAGA Ras-related GTP binding A parous good SAPS3 SAPS domain family, member 3 parous good SCNN1B sodium channel, nonvoltage-gated 1, beta nulliparous good SHMT2 serine hydroxymethyltransferase 2 (mitochondrial) nulliparous good SKA1 chromosome 18 open reading frame 24 parous good SLC25A32 solute carrier family 25, member 32 nulliparous good SRP19 signal recognition particle 19 kDa nulliparous good ST20 suppressor of tumorigenicity 20 parous good STAU1 staufen, RNA binding protein, homolog 1 (Drosophila) nulliparous good STX3 syntaxin 3 nulliparous good THAP4 THAP domain containing 4 parous good TIMELESS timeless homolog (Drosophila) nulliparous good TMC01 transmembrane and coiled-coil domains 1 nulliparous good TMED9 transmembrane emp24 protein transport domain containing 9 nulliparous good TMEM158 transmembrane protein 158 nulliparous good TMEM222 transmembrane protein 222 parous good TOB1 transducer of ERBB2, 1 nulliparous good TSPAN13 tetraspanin 13 nulliparous good TTC38 tetratricopeptide repeat domain 38 parous good TUBA1C tubulin, alpha 1c nulliparous good TXNDC9 thioredoxin domain containing 9 nulliparous good UBA2 ubiquitin-like modifier activating enzyme 2 nulliparous good UQCRB similar to ubiquinol-cytochrome c reductase binding protein nulliparous good WDR12 WD repeat domain 12 nulliparous good XPOT exportin, tRNA (nuclear export receptor for tRNAs); similar to Exportin-T (tRNA nulliparous good exportin) (Exportin(tRNA)) YEATS4 YEATS domain containing 4 nulliparous good YIF1A Yip1 interacting factor homolog A (S. cerevisiae) nulliparous good ZDHHC14 zinc finger, DHHC-type containing 14 parous good ZFAND1 zinc finger, AN1-type domain 1 nulliparous good ZNF217 zinc finger protein 217 nulliparous good ZNF264 zinc finger protein 264 nulliparous good ZNF304 zinc finger protein 304 nulliparous good ZNF706 zinc finger protein 706 nulliparous good ZWINT ZW10 interactor nulliparous good

Example 10 Parity-Associated Decrease in Mammary Epithelial Progenitors and Breast Tumor Initiation

The data described in the Examples above support the hypothesis that a decrease in the number and proliferative potential of luminal progenitors in parous women directly relates to a decrease in breast cancer risk for both ER+ and ER− breast cancers, and that this effect is dependent on the age at first full-term pregnancy. A mathematical model of the dynamics of proliferating mammary epithelial cells was designed that can accumulate the changes leading to cancer initiation. In the model, described in detail below, two types of cells were considered: (1) a self-renewing population of stem cells and, (2) a population of proliferating hormone-responsive luminal progenitors that result from the differentiation of these stem cells.

Mathematical Modeling:

Simulations were initiated at menarche and continued until cancer initiation or death, as depicted in the timeline in FIG. 30. The effect of pregnancy at varying times from menarche through right before menopause on cancer initiation was tested and compared against the nulliparous cancer initiation risk. The robustness of the simulation over varying numbers of stem cells per terminal end duct, additional proliferative capacities resulting from pregnancy, and rates of asymmetric stem cell division were then tested.

The dynamics of stem cells in the breast ductal system was first studied. Given the population structure inherent to breast ducts, it was assumed that the stem cells in each duct act independently. As such, the dynamics of a single duct within the breast was investigated since the total probability of cancer initiation is given by the probability per niche times the number of niches. Thus, the relative likelihood of cancer initiation is not altered by considering only one niche. The overall number of stem cells in the breast is on the order of 5 to 10 cells per duct, and this number was denoted by N. A fundamental time step of this system to be dictated by the division time of stem cells, t_(step), which varies during pregnancy, was defined. In previously published in vivo experiments, the mean cell cycle length of benign breast hyperplasia cells was approximately 162 hours per cell. It was assumed that even benign breast hyperplasia cells divide faster than stem cells; thus, using t_(step)=162 hours as the average stem cell cycle length when not pregnant may be an overestimation of the number of stem cell divisions that occur in the normal breast. Within a duct, a single stem cell is randomly chosen to divide during each time step proportional to the fitness of the cell, following a stochastic process known as the Moran model (see, Moran, P. A. P. (1962). The statistical processes of evolutionary theory (Oxford: Clarendon Press). National Center for Health Statistics (US) (2012). Health, United States, 2011: With Special Feature on Socioeconomic Status and Health (Hyattsville, Md.)). According to this model, the divided cell is replaced by one of the daughter cells of the division, while the other daughter replaces another stem cell that was randomly selected from the population. Use of this model ensured preservation of homeostasis in the normal breast cell population. For each cell division, a single mutation was allowed to arise in one of the two daughter cells of the division.

In the mature breast, stem cells divide primarily to maintain cellular integrity. However, differentiating events do occur, although rarely. In this model, with probability p, cell division in the current time step was allowed to be asymmetric, producing one stem daughter cell to maintain the stem cell population and one progenitor daughter. Since the exact rate of differentiation is unknown, p=10⁻¹ to 10⁻³ was tested. With the remaining 1-p probability, the stem cell division is symmetric and followed the usual Moran division dynamics. In each time step thereafter, all of the cells resulting from the progenitor daughter divided and differentiated further until a total of z cell divisions were accumulated. We set z=10, to fit data from mouse fat pad depletion experiments (see, Kordon, E. C., and Smith, G. H. (1998). An entire functional mammary gland may comprise the progeny from a single cell. Development 125, 1921-1930.) After z_(pre) divisions, the cells were considered differentiated and, at this point, they were no longer included in the cells considered in the mathematical model. Thus, in the wild-type system, there were N stem cells per duct and 2^(z+1)−1 progenitor cells per differentiation cascade. FIG. 34 describes the temporal dynamics of the system.

During each cell division, genetic alterations contributing to cancer initiation may arise. A number n_(mut) of mutations were considered that, when combined, result in a single cell leading to cancer initiation. These mutations could be any of the many mutations commonly found in breast cancer with initiation potential; however, it was assumed that only a single mutational hit was necessary to (in)activate the gene. The simulation was tested with mutation rates on the order of 10⁻⁵ mutations per gene per cell division to limit the required number of simulations for detection to a reasonable number; however, results remained consistent even at lower mutation rates. The following mutational effects were assumed for each mutation: in stem cells, mutant cells had a relative fitness of f_(mut)=1.1, i.e. a fitness increase of 10%, resulting in an increased probability of dividing, while mutant progenitor cells divided an additional z_(mut)=1 times (FIG. 34). Since the number of stem cells per duct is small, the fitness of mutant alleles has little effect on cancer initiation probabilities, as the fixation time of mutations is much smaller than the mutation accumulation time (see, Hambardzumyan, D., Cheng, Y. K., Haeno, H., Holland, E. C., and Michor, F. (2011). The probable cell of origin of NF1- and PDGF-driven glioblastomas. PLoS One 6, e24454). Thus, ignoring the specific value of f_(mut) is justified. These assumptions presume that the mutations primarily act to increase the proliferation rate of cells. Mutant fitness values were considered to be multiplicative while mutant progenitor division capacity was considered to be additive. Thus, the relative fitness of a stem cell with n mutations was f_(mut) ^(n) and the number of divisions a mutant progenitor with n mutations was z+n*z_(mut). Additionally, progenitor cells must accumulate some propensity towards self-renewal: a parameter γ=γ_(base)−i*γ_(step) was defined as the probability of a progenitor cell at differentiation level 0≦i≦z+n*z_(mut) acquiring self-renewal. Cancer initiation was defined as a single cell that accumulated all required mutations and either retained or acquired the ability to self-renew, either through being a stem cell or through acquiring a genetic or epigenetic self-renewal event.

The phenotypic alterations that occur in the breast during pregnancy and as a result of pregnancy were considered. For the purposes of this simulation, the 280 day period of time for the pregnancy itself was considered as the time period during which parameters are altered by pregnancy. It has been previously published that pregnancy results in terminal differentiation of progenitor cells into milk producing cells as well as increased proliferation of cells. To model these effects, further differentiation of progenitor cells during pregnancy by an additional z_(preg) differentiation levels, and a decrease in the cell cycle length of stem cells was allowed (FIG. 34). According to several groups, there is a 4.5 to 8.5-fold increase in Ki67+ cells during pregnancy. Thus, a 4-fold to 8-fold increase in progenitor cells during pregnancy was allowed, corresponding to Z_(preg)=2 to 3. The remaining ˜1.1 fold increase in proliferation was modeled as a decrease in stem cell cycle length to t_(step,preg)=147 hours. Additionally, as described in the Examples, above, there was also a decrease in the number of proliferative progenitors after pregnancy: this change was simulated in population structure by decreasing the number of differentiation levels in the progenitor hierarchy by z_(post). The experiments showed a 2-3 fold drop in p27⁺ expressing progenitor cells, which would correspond to z_(post)=1.

The simulation spanned from menarche to death or initiation of cancer within the duct. As such, the total simulation time was calculated from the average women's life expectancy in the United States, which was 80.9 years in 2009, and the average age of menarche, which ranged between 12.4-12.7 years of age for differing age groups in 2002 (FIG. 34). The mean age of menarche between the groups was used, which was 12.6 years, and thus resulted in a total of 68.3 years of simulation time. The effects of pregnancy occurring at four roughly equidistant time points, t_(preg) was tested: immediately following menarche, time of first pregnancy at the average age of 25.4 in 2010, immediately before menopause at the average age of 51.3 in 1998, and halfway between average first pregnancy and menopause at the age of 38.3. All time points were tabulated from the most recent government-provided data. The effects of varying the simulation parameters independently for each pregnancy age t_(preg) were tested. All fixed value parameters and the values of all other parameters are listed in the tables below.

TABLE 19 Fixed parameter values t_(total) t_(step) t_(step, preg) (years) f_(mut) γ γ_(step) μ (h) (h) z z_(mut) z_(post) 68.3 1.1 0.1 0.005 2 × 162 147 10 1 −1 10⁻⁵ Legend: Parameters that remained unchanged throughout all simulations are shown.

TABLE 20 Range of parameter values investigated t_(preg) N n_(mut) p z_(preg) 0 5 1 10⁻³ 2 12.8 8 2 10 ⁻² 3 25.7 10 10⁻¹ 38.7 Legend: For each parameter of interest, multiple values were tested. Values defaulted to the numbers in bold.

In the schematic depicted in FIG. 31, initially, there are N wild-type stem cells (top of schematic), which give rise to a differentiation cascade of 2^(z+1)−1 wild-type luminal progenitor cells (triangular, lower region). At each time step, all progenitor cells as well as one randomly selected stem cell divide. With probability a, the stem cell divides symmetrically and one daughter cell replaces another randomly chosen stem cell. With probability 1-α, the stem cell divides asymmetrically and one daughter cell remains a stem cell while the other daughter cell becomes committed to the progenitor population. Regardless of the dividing stem cell's fate, all existing progenitor cells divide symmetrically for a total of z times to give rise to successively more differentiated cells (progressively darker shades of gray) before becoming terminally differentiated. Darkening gray gradations refer to successively more differentiated cells and serve to clarify a single time step of the stochastic process.

In FIG. 32, the acquisition of mutations leading to breast cancer initiation all result in an increased relative fitness (i.e., growth rate) f_(mut) in stem cells (“SC”) as compared to wild-type cells (“WT”) and an additional number of divisions z_(mut) progenitor cells can undergo before terminally differentiating.

In FIG. 33, during pregnancy, progenitor cells experience an expansion in proliferative capacity through an additional number of division Z_(preg) in order to form terminally differentiated milk-producing cells (dotted triangle) and a decrease in cell cycle length.

The effect of pregnancy on breast cancer per duct (expressed as the relative probability of cancer initiation) as compared to nulliparous simulations initiation at varying times after menarche was tested and compared to the risk of tumor initiation in nulliparous women. Default values were N=8, p=10⁻², Z_(preg)=2 (FIG. 34). It was observed that the relative likelihood of initiation increased with later pregnancy. The robustness of the simulation over varying numbers of stem cells per terminal end duct, additional proliferative capacities resulting from pregnancy, and rates of asymmetric stem cell division were tested (FIGS. 35-37). The relative likelihood of cancer initiation was then compared with pregnancy occurring at four different time points during childbearing years as compared to nulliparous simulations. It was found that the probability of cancer initiation in a duct increases as the age at first pregnancy increases. Furthermore, these simulations showed that differences in the numbers of luminal epithelial progenitors with proliferative potential is the most probable explanation for differences in breast cancer risk due to reproductive (e.g., parity) and genetic (e.g., BRCA1/2 germline mutation) factors.

In summary, it was found that both increasing numbers of stem cells per duct and increasing rates of asymmetric stem cell division increase the rate of cancer initiation per duct. Also, as expected, changes in the proliferative capacity of progenitor cells during pregnancy had no effect in the nulliparous state. The relative likelihood of cancer initiation was then compared with pregnancy occurring at four different time points during a woman's childbearing years as compared to the nulliparous simulations. It was found that the probability of cancer initiation in a duct increases as the age of first pregnancy increases within the range of all simulated parameters. Additionally, the probability of cancer initiation is greater in nulliparous situations than in all pregnancy simulations. Interestingly, cancer initiation from the stem cell population decreases with age of first pregnancy while initiation from progenitors increases. Some of the cancers that were considered as initiated from the progenitor population may potentially have had a stem initiation event occur afterwards, and simulations where progenitor initiation occurred are also those where fixation of the first mutation in the stem population was likely.

A number of embodiments of the invention have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the invention. It is further to be understood that all values are approximate, and are provided for description. Accordingly, other embodiments are within the scope of the following claims.

LENGTHY TABLES The patent application contains a lengthy table section. A copy of the table is available in electronic form from the USPTO web site (http://seqdata.uspto.gov/?pageRequest=docDetail&DocID=US20150285802A1). An electronic copy of the table will also be available from the USPTO upon request and payment of the fee set forth in 37 CFR 1.19(b)(3). 

1. A method of predicting a subject's risk of developing breast cancer, wherein the method comprises: (a) determining, in a breast tissue sample from a subject, the frequency of CD44+, CD24− breast epithelial cells or the frequency of CD44+ breast epithelial cells with an assay that comprises the use of one or both of an antibody that binds specifically to CD44 and an antibody that binds specifically to CD24, and (b) predicting that the subject has a relatively elevated risk of developing breast cancer if the frequency of CD44+, CD24− breast epithelial cells is decreased compared to a first control frequency of CD44+, CD24− breast epithelial cells; or predicting that the subject has a relatively reduced risk of developing breast cancer if the frequency of CD44+ breast epithelial cells is increased compared to a second control frequency of CD44+, CD24− breast epithelial cells.
 2. The method of claim 1, further comprising determining the frequency of CD24+ breast epithelial cells.
 3. The method of claim 2, wherein step (b) comprises: predicting that the subject has a relatively elevated risk of developing breast cancer if: (i) the frequency of CD44+, CD24− breast epithelial cells is decreased compared to the first control frequency of CD44+, CD24− breast epithelial cells, and (ii) the frequency of CD24+ breast epithelial cells is increased compared to a first control frequency of CD24+ breast epithelial cells; or predicting that the subject has a relatively reduced risk of developing breast cancer if: (i) the frequency of CD44+_breast epithelial cells is increased compared to the second control frequency of CD44+, CD24− breast epithelial cells, and (ii) the frequency of CD24+ breast epithelial cells is decreased compared to a second control frequency of CD24+ breast epithelial cells.
 4. The method of claim 2, wherein step (b) comprises: predicting that the subject has a relatively elevated risk of developing breast cancer if the frequency of CD24+ breast epithelial cells is greater than the frequency of CD44+, CD24-breast epithelial cells in the sample; or predicting that the subject has a relatively reduced risk of developing breast cancer if the frequency of CD24+ breast epithelial cells is equal to or less than the frequency of CD44+, CD24− breast epithelial cells in the sample.
 5. The method of claim 1, wherein the subject is in need of such predicting.
 6. A method of predicting a subject's risk of developing breast cancer, wherein the method comprises: (a) determining the frequency in a breast tissue sample of cells of one or more types selected from the group consisting of p27+ breast epithelial cells, Sox17+ breast epithelial cells, Cox2+ breast epithelial cells, Ki67+ breast epithelial cells, ER+, p27+ breast epithelial cells, ER+, Sox17+ breast epithelial cells, ER+, Cox2+ breast epithelial cells, ER+, Ki67+ breast epithelial cells; androgen-receptor-positive (AR+), p27+ breast epithelial cells, AR+, Sox17+ breast epithelial cells, AR+, Cox2+ breast epithelial cells, and AR+, Ki67+ breast epithelial cells with an assay that comprises the use of one or more antibodies selected from the group consisting of: an antibody that binds specifically to p27, an antibody that binds specifically to Sox17, an antibody that bind specifically to Cox2, an antibody that binds specifically to Ki67, an antibody that binds specifically to ER, and an antibody that specifically binds to AR; and (b) predicting that the subject has a relatively elevated risk of developing breast cancer if the frequency of the cells of one or more types is about the same or increased compared to a first control frequency of cells of the one or more types, respectively; or predicting that the subject has a relatively reduced risk of developing breast cancer if the frequency of the cells of the one or more types is decreased compared to a second control frequency of the cells of the one or more types, respectively.
 7. The method of claim 6, wherein the frequency of p27+ breast epithelial cells is determined, and the first control frequency of the p27+ breast epithelial cells is a level that represents 15%, 20%, or 25% of the breast epithelial cells in the sample, and the second control frequency of p27+ breast epithelial cancer cells is a level that represents 15%, 20%, or 25% of the breast epithelial cells in the sample. 8.-9. (canceled)
 10. The method of claim 6, wherein the frequency of Ki67+ breast epithelial cells is determined, the first control frequency of the Ki67+ breast epithelial cells is a level that represents 2% of the breast epithelial cells in the sample, and the second control frequency of Ki67+ breast epithelial cells is a level that represents 2% of the breast epithelial cells in the sample.
 11. (canceled)
 12. A method of predicting a subject's risk of developing breast cancer, wherein the method comprises: (a) determining the protein or mRNA expression level in a breast tissue sample from a subject of at least one marker selected from the group consisting of p27, Sox17 and Cox2; and (b) predicting that the subject has a relatively elevated risk of developing breast cancer if the protein or mRNA expression level of the at least one marker is increased compared to a first control level of the at least one marker; or predicting that the subject has a relatively reduced risk of developing breast cancer if the protein or mRNA expression level of the at least one marker is decreased compared to a second control level of the at least one marker. 13.-14. (canceled)
 15. The method of claim 12, wherein step (a) further comprises determining the protein or mRNA expression level of one or more additional markers having an expression level that is modulated in breast epithelial cells of parous women compared to the levels in breast epithelial cells of nulliparous women.
 16. The method of claim 12, wherein the sample is enriched for CD44+, CD24− breast epithelial cells, Ki67+ breast epithelial cells, CD44+Ki67+ breast epithelial cells, or CD24+ breast epithelial cells prior to the determining. 17.-19. (canceled)
 20. The method of claim 1, wherein the subject has a BRCA1 mutation.
 21. The method of claim 1, wherein the subject has a BRCA2 mutation.
 22. The method of claim 12, wherein step (a) comprises determining the protein or mRNA expression level of at least two markers selected from the group consisting of p27, Sox17 and Cox2.
 23. The method of claim 22, wherein step (a) comprises determining the protein or mRNA expression level of p27, Sox17, and Cox2.
 24. A method of predicting a subject's risk of developing breast cancer, the method comprising: determining a parity/nulliparity-associated mRNA expression signature in a sample comprising breast epithelial cells from a subject; and predicting a subject's risk of developing breast cancer based on the determined parity/nulliparity-associated mRNA expression profile in the sample.
 25. The method of claim 24, wherein the sample is enriched for CD44+ cells, CD24+ cells, or CD10+ cells. 26.-39. (canceled)
 40. The method of claim 1, wherein the breast cancer is an ER+ breast cancer.
 41. The method of claim 1, wherein the breast cancer is an ER− breast cancer. 